KEYNOTE TALKS (UTC+0)
| Keynote talk I | Saturday 14.12.2024 | 08:45 - 09:35 | Room: Auditorium |
| Bayesian modeling in neuroimaging: Brain networks dynamics | |||
| Speaker: M. Guindani | Chair: Mario Peruggia | ||
| Keynote talk II | Saturday 14.12.2024 | 15:30 - 16:20 | Room: Auditorium |
| Towards interpretable and trustworthy network-assisted prediction | |||
| Speaker: L. Levina Co-authors: R. Lunde, T. Tang, J. Zhu | Chair: Matthieu Marbac | ||
| Keynote talk III | Monday 16.12.2024 | 11:20 - 12:10 | Room: Auditorium |
| Regression modelling under general heterogeneity | |||
| Speaker: G. Kapetanios Co-authors: L. Giraitis, Y. Li | Chair: Michael Pitt | ||
| Keynote talk IV | Monday 16.12.2024 | 13:40 - 14:30 | Room: Auditorium |
| Statistics for complex data objects - of brain structures, cell shapes and income share distributions | |||
| Speaker: S. Greven | Chair: Kalliopi Mylona | ||
PARALLEL SESSIONS (UTC+0)
| Parallel session B: CFECMStatistics2024 | Saturday 14.12.2024 | 10:05 - 12:10 |
| Session CI051 (Special Invited Session) | Room: Auditorium |
| High-dimensional time series | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Tommaso Proietti | Organizer: Tommaso Proietti |
| A0463: Y. Liu, K. Fujimori, Y. Goto, M. Taniguchi | |
| Sparse principal component analysis for high-dimensional stationary time series | |
| A1287: G. Motta, M. Eichler | |
| Frequency-domain estimation of dynamic factor models | |
| A1330: S. Tonini, A. Vandin, F. Chiaromonte, D. Licari, F. Barsacchi | |
| Accurate and fast anomaly detection in industrial processes |
| Session CO350 | Room: K0.16 |
| Statistical analysis of networks and applications | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Wendy Meiring | Organizer: Wendy Meiring |
| A1604: W. Meiring | |
| Networks in neuroscience: Functional and structural brain connectivity | |
| A1270: R. Liu, G. Yu | |
| Covariate-adjusted Gaussian graphical models via natural parametrization | |
| A1406: Y. Jiang | |
| Online graph topology learning from matrix-valued time series | |
| A1590: S. Achard, I. Gannaz | |
| Networks inference with (quasi-)analytic wavelets | |
| A1684: G. Di Luzio, G. Morelli | |
| High-dimensional semiparametric skew-elliptical copula graphical models |
| Session CO279 | Room: K0.19 |
| Complex environmental data and modeling (CoEnv) | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Nicola Pronello | Organizer: Nicola Pronello, Pasquale Valentini |
| A0308: I. Marques, P. Wiemann, M. Katzfuss | |
| Scalable additive Gaussian process regression using Vecchia approximations | |
| A0642: C. Zaccardi, P. Valentini, L. Ippoliti | |
| Confounding adjustment with spatiotemporal data | |
| A0696: R. ODonnell, M. Scott, C. Miller, I. Paun | |
| Unlocking insights into UK rivers using statistics and data analytics | |
| A1082: R. Ignaccolo, N. Pronello, A. Cucco, V. Frontuto, N. Golini, L. Ippoliti | |
| Estimating graphical models varying on a spatial network for water quality assessment |
| Session CO376 | Room: K0.50 |
| Factorial designs under model uncertainty | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Steven Gilmour | Organizer: Steven Gilmour |
| A1243: S. Gilmour | |
| An overview of the $Q\_B$-optimality criterion | |
| A0334: A. Vazquez, P. Goos, W. Wong | |
| Constructing two-level QB-optimal screening designs using mixed-integer programming and heuristic algorithms | |
| A1173: P.-W. Tsai, S. Gilmour | |
| Optimal two-level designs under model uncertainty | |
| A0913: M.S. Ismail Hameed, E. Schoen, J. Nunez Ares, P. Goos | |
| A complete catalog of D- and A-optimal designs with up to 20 runs | |
| A0262: X. Zhou, S. Gilmour | |
| Optimal designs under model uncertainty |
| Session CO036 | Room: K2.40 |
| High dimensional multivariate models with applications | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Etienne Marceau | Organizer: Etienne Marceau |
| A1386: P. Semeraro, R. Fontana | |
| Classes of high dimensional Bernoulli distributions and applications | |
| A1395: A. Mutti, H. Cossette, E. Marceau, P. Semeraro | |
| Convex bounds on sums with generalized FGM copula | |
| A1434: P. Ribereau, V. Maume-Deschamps, M. Ahmed | |
| Model selection for extremal dependence structures using deep learning: Application to environmental data | |
| A1627: M. Mailhot, M. Michaelides | |
| Conditional spatiotemporal copula model for crop insurance | |
| A1637: C. Bernard, J. Chen, L. Ruschendorf, S. Vanduffel | |
| Coskewness under dependence uncertainty |
| Session CO028 | Room: K2.41 |
| Recent advances in structural equation modelling | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Andrej Srakar | Organizer: Andrej Srakar |
| A1257: S. van Erp, P.-C. Burkner, A. Vehtari | |
| Projection predictive variable selection for Bayesian regularized SEM | |
| A1327: T. Schamberger, F. Schuberth, Y. Rosseel, J. Henseler | |
| A maximum likelihood estimator for composite models | |
| A1336: T. Jorgensen, W.W. Loh | |
| A numerical procedure to estimate dynamic treatment regimes from observational data using structural equation modeling | |
| A1348: Y. Rosseel | |
| The structural-after-measurement (SAM) approach to SEM | |
| A1501: J. Moss, N. Foldnes, S. Gronneberg | |
| Improved goodness of fit procedures for structural equation models |
| Session CO011 | Room: S0.11 |
| Improving statistical image analysis | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Ranjan Maitra | Organizer: Ranjan Maitra |
| A0254: C. Llosa, R. Maitra | |
| Elliptically-contoured tensor-variate distributions with application to improved image learning | |
| A0978: I. Almodovar Rivera | |
| Incorporating seasonality in fMRI time series to address the learning effect | |
| A0551: D. Adrian, R. Maitra, D. Rowe | |
| Improved activation detection from magnitude and phase functional MRI data | |
| A0687: A. Thomas, M. Jauch, D. Matteson, P. Crozier | |
| Topological data analysis for statistical analysis of structure and dynamics in imaging | |
| A0716: A. Murua, R. Maitra | |
| Approximations in the Ising model for use in scene analysis |
| Session CO170 | Room: S-1.01 |
| HiTEc: Advances in financial econometrics | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Genaro Sucarrat | Organizer: Genaro Sucarrat |
| A0878: S. Campos Martins | |
| On the credibility of the 2015 Paris agreement and effectiveness of climate policies | |
| A0981: F. Violante, S. Grassi | |
| An economic evaluation of exchange rates higher order moments timing | |
| A0856: J.-M. Zakoian, C. Francq, L. Trapani | |
| Testing for breaks in the conditional mean based on the estimating function approach | |
| A1475: H. Veiga, J.M. Marin, E. Romero | |
| A HAR-based stochastic volatility model for leverage propagation | |
| A0521: G. Sucarrat | |
| Volatility prediction under misspecification |
| Session CO005 | Room: S-1.04 |
| Stochastic processes: Theory and applications | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Lorenzo Mercuri | Organizer: Lorenzo Mercuri |
| A0482: H. Masuda, M. Delattre | |
| Profile quasi-likelihood inference for SDE with mixed effects | |
| A0655: T. Ogihara, M. Stadje | |
| Efficient drift parameter estimation for ergodic solutions of backward SDEs | |
| A0755: A. Barbiero | |
| Yet another approximation for the total claims amount using the Weibull distribution | |
| A1089: E. Rroji, L. Mercuri, I. stefani | |
| The greenium term structure | |
| A1329: Y. Iguchi, A. Beskos | |
| Parameter inference for hypo-elliptic diffusions under a weak design condition |
| Session CO271 | Room: BH (S) 2.01 |
| Modeling financial time series with conometrics and machine learning | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Markus Haas | Organizer: Markus Haas |
| A0322: L. Catania | |
| A new way to specify dynamic models | |
| A0739: D. Umlandt | |
| Observation-driven filtering of the term premium | |
| A0817: A. Teller, U. Pigorsch, C. Pigorsch | |
| Realized volatility forecasting for new issues and spin-offs using multi-source transfer learning | |
| A0678: M. Segnon | |
| Supply chain disruptions and foreign exchange rate volatility | |
| A0935: M. Haas | |
| A multiple chains hidden Markov model for a sector index and its comovement with the market |
| Session CO205 | Room: BH (S) 2.02 |
| Recent advances in well-being and poverty measurement | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Chiara Gigliarano | Organizer: Mariateresa Ciommi, Chiara Gigliarano |
| A0313: F. Mariani, M.C. Recchioni, M. Ciommi, C. Gigliarano | |
| A new point of view in the construction of composite indicators: A case study | |
| A0514: A. D Agostino, L. Neri, A. Regoli, G. Betti, F. Tavares | |
| Regional comparison of household well-being: Insights from Lombardy, Tuscany, and Campania | |
| A0874: A. Sarra, E. Nissi, A. Evangelista, T. Di Battista | |
| Dynamic approaches to ranking happiness: Integrating benefit of the doubt weighting and functional data analysis | |
| A0877: J.L. Garcia-Lapresta | |
| Combining quantitative and qualitative assessments in the multidimensional well-being measurement | |
| A1076: A. Bianchi, C. Gigliarano, S. Maiorino | |
| Inner areas in Lombardy: An analysis of vulnerability dynamics |
| Session CO196 | Room: BH (S) 2.03 |
| Advances in Bayesian macro- and financial econometrics | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Toshiaki Watanabe | Organizer: Toshiaki Watanabe |
| A0244: J. Nakajima | |
| Estimating trend inflation in a regime-switching Phillips curve | |
| A0221: T. Ishihara | |
| High-dimensional multivariate realized stochastic volatility model using characteristic factor regression | |
| A0184: D. Hiraki, S. Chib, Y. Omori | |
| Stochastic volatility in mean: Efficient analysis by a generalized mixture sampler | |
| A0644: Y. Ueno | |
| Linkage between wage and price inflation in Japan | |
| A0722: M. So, S.H. Chan, A. Chu | |
| Multi-view dynamic network modeling |
| Session CO069 | Room: BH (SE) 1.01 |
| Advances in high/infinite-dimensional inference (virtual) | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Catia Scricciolo | Organizer: Catia Scricciolo |
| A0894: R. Altmeyer | |
| Polynomial time guarantees for sampling based posterior inference | |
| A0918: S. Banerjee | |
| Recent advances in high-dimensional Bayesian graphical models | |
| A0286: C. Li, S. Sun, Y. Zhu | |
| Bayesian fixed-domain posterior contraction for spatial Gaussian process model with nugget | |
| A0183: M. Neykov | |
| On the minimax rate of the Gaussian sequence model under (bounded) convex constraints | |
| A0522: D. Nieman | |
| Variational Bayesian procedures with frequentist guarantees |
| A0353: M. Moores, S. Fonseka, J. Hansen, D. Gunawan | |
| Bayesian mixture of spectral density functions for ocean waves | |
| A0630: D. van Dyk | |
| Bayesian mixture models for scientific discovery in astrophysics | |
| A0693: A. Giampino, A. Canale, B. Nipoti | |
| Bayesian co-clustering of ordinal data with informative censoring | |
| A0762: L. D Angelo, F.Z. Ricci | |
| Flexible modeling of grouped multivariate data via Bayesian shared-atom nested mixture models | |
| A0778: M. Stival, A. Andreella, L. Schiavon, S. Campostrini | |
| Combining Bayesian latent traits and topic models for identifying risky behavioral profiles in Italian population |
| A1355: S. Sosvilla-Rivero, J. Andrada-Felix, M. Gomez-Puig | |
| Assessing the ESG premium: Evidence from Spain | |
| A1443: L. Sanchis-Marco, L. Garcia-Jorcano | |
| Measuring the impact of biodiversity loss on financial markets: A CoVaR approach | |
| A1512: J.-A. Jimenez-Martin, L. Garcia-Jorcano, M.D. Robles | |
| A tale of dynamic tail climate transition risk exposure: TCaRE | |
| A1504: A.M. Garcia Sanz, J.-A. Jimenez-Martin, M.D. Robles | |
| Does the gender diversity in the nexus with sustainability affect downside and tail risks | |
| A1568: E. Ballesta, P. Abad, M.-D. Robles | |
| Financing biodiversity: Does a biodiversity premium exist in sustainable bonds |
| Session CO159 | Room: BH (SE) 1.06 |
| Advances in Bayesian methods | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Lucia Paci | Organizer: Lucia Paci |
| A0440: F. Castelletti, L. Ferrini | |
| Bayesian nonparametric mixtures of categorical directed graphs for heterogeneous causal inference | |
| A0561: P. Behrouzi, V. Vinciotti, R. Mohammadi | |
| Bayesian structural learning with parametric marginals for count data: An application to microbiota systems | |
| A1230: B. Nipoti, L. D Angelo, A. Ongaro | |
| Dependent Dirichlet processes via thinning | |
| A0186: L. Rimella, M. Whitehouse, N. Whiteley | |
| Consistent and fast inference in compartmental models of epidemics using Poisson approximate likelihoods |
| Session CO307 | Room: BH (SE) 2.05 |
| Forecasting: Theory and practice | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Daniele Girolimetto | Organizer: Daniele Girolimetto |
| A0454: P. Duttilo, F. Lisi, M. Bertolini | |
| Impact of grid innovations on electricity price volatility in Italian island markets | |
| A0464: F. Petropoulos | |
| Fast forecast reconciliation using sub-hierarchies | |
| A0759: R. Hollyman | |
| Scalable dynamic hierarchical forecast reconciliation | |
| A0902: N. Kourentzes, G. Athanasopoulos | |
| On the difference of the existing hierarchical forecasting approaches | |
| A0406: D. Girolimetto, T. Di Fonzo | |
| Coherent forecast combination for linearly constrained multiple time series |
| Session CO247 | Room: BH (SE) 2.09 |
| Inequality and macroeconomic dynamics | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Nao Sudo | Organizer: Nao Sudo |
| A1389: T. Yamada, N. Sudo, M. Inui | |
| The effects of monetary policy shocks on inequality in Japan | |
| A1404: B. Chafwehe, M. Ricci, D. Stoehlker | |
| The impact of the cost-of-living crisis on European households | |
| A1410: T. Lee | |
| New golden rule: K and L returns | |
| A1413: H. Ito, J. Aizenman | |
| Wealth inequality and economic volatilities | |
| A1687: Y. Terajima, C. Wilkins | |
| Cyclicality of income growth distribution and the role of monetary policy |
| Session CO233 | Room: BH (SE) 2.10 |
| Recent developments in the econometrics of commodity markets | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Malvina Marchese | Organizer: Malvina Marchese |
| A0301: J. Chevallier, D.-T. Vo | |
| Navigating the French stock market using nonlinear quantitative investing methods | |
| A0327: M. Risstad, M. Marchese, A. alizadeh | |
| A novel hybrid ensemble approach to forecast freight rates volatility | |
| A0335: M. Mazzanti | |
| Efficient semiparametric estimation of environmental and climate policy | |
| A0343: T. Franus, M. Andrew, J. Culley | |
| Valuation of the leasehold properties in the England using a machine learning approach | |
| A1187: M. Bonato | |
| Shortages to forecast aggregate and sectoral U.S. stock market realized variance |
| Session CO218 | Room: BH (SE) 2.12 |
| Advances in panel data and causal inference | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Laura Liu | Organizer: Laura Liu |
| A0397: A. Zeleneev, T. Armstrong, M. Weidner | |
| Robust estimation and inference in panels with interactive fixed effects | |
| A0648: L. Liu, I. Botosaru | |
| Time-varying heterogeneous treatment effects in event studies | |
| A0719: F. DiTraglia, E. Karger, C. Garcia Jimeno | |
| To link or not to link: Estimating long-run treatment effects from historical data | |
| A1019: L. Sun, E. Ben-Michael, A. Feller | |
| Using multiple outcomes to improve the synthetic control method | |
| A1367: W. Wang | |
| Rank assisted network regression |
| Session CO321 | Room: Safra Lec. Theatre |
| Snapshot on current functional data methodologies | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Frederic Ferraty | Organizer: Frederic Ferraty |
| A0218: G. Cao | |
| Empowering multi-class classification for complex functional data with simultaneous feature selection | |
| A0230: S. Otto, L. Winter | |
| Factor-augmented functional regression with an application to electricity price curve forecasting | |
| A0617: M. Carey, J. Ramsay | |
| Penalized regression models informed by physics | |
| A1067: A. Srivastava | |
| On advances in shape-based functional data analysis | |
| A1345: S. Wang, V. Patilea | |
| Fast rate estimation of integrals of multivariate random functions |
| Session CO126 | Room: K2.31 (Nash Lec. Theatre) |
| New advances in small area estimation | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Francesco Schirripa Spagnolo | Organizer: Francesco Schirripa Spagnolo |
| A0339: L. Perfetti Villa, N. Tzavidis, A. Luna Hernandez | |
| On the use of small area estimation with geospatial data | |
| A1338: D. Morales, E. Cabello, M.-D. Esteban, A. Perez Martin | |
| Small area estimation under bivariate Fay-Herriot model with correlated random effects | |
| A0457: S. Ranjbar, K. Reluga, N. Salvati, D. Kong, M. van der Laan | |
| Causal small area estimation: The impact of job stability on monetary poverty in Italy | |
| A0263: M. Bugallo, D. Morales, F. Schirripa, N. Salvati | |
| M-quantile regression for zero-inflated data and its applications to small area estimation | |
| A0980: N. Frink | |
| Small area estimation with quantile regression forests |
| Session CO246 | Room: BH (S) 1.01 Lec. Theathre 1 |
| Recent contributions in applied econometrics | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Pipat Wongsa-art | Organizer: Pipat Wongsa-art |
| A0541: V. Serra-Sastre, C. Nicodemo | |
| The impact of Brexit on ethnic discrimination among NHS Workforce | |
| A0614: Y. Xu | |
| Almost unbiased variance estimation in IV regression | |
| A0656: M. Knowles | |
| The Nash wage elasticity and its business cycle implications | |
| A0920: E. Hill, L. Giraitis | |
| Parametric Whittle estimation of cyclically integrated time series | |
| A0749: P. Wongsa-art | |
| A new model for agricultural land use modelling and prediction in England using spatially high-resolution data |
| Session CC446 | Room: K0.18 |
| Applied statistics | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Joachim Schnurbus | Organizer: CFE-CMStatistics |
| A0823: J.-M. Poggi, B. Portier, M. Bobbia | |
| Spatial correction of low-cost sensors observations for fusion of air quality measurements | |
| A1286: R. Molinari, S. Guerrier, N. Mili, Y. Yavuz-Ozdemir, S. Orso, C. Miglioli, G. Bakalli | |
| SWAG: Interpretation, replicability and statistical inference with multiple simple models | |
| A1346: C. Lupi | |
| Benford's law(s) and power law distributions | |
| A1575: A. Ciarleglio | |
| Estimating treatment decision rules for ordinal outcomes with applications to an antidepressant treatment trial | |
| A1305: T. Stindl, J. Kwan, F. Chen, Y. Guan | |
| Modelling gunfire in Washington, D.C. using a spatiotemporal Hawkes process with nonseparable triggering function |
| Session CC478 | Room: K0.20 |
| Robust statistics | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Andreas Artemiou | Organizer: CFE-CMStatistics |
| A0224: A. Garcia-Perez | |
| A novel robust weighted least squares regression line | |
| A1216: T. Kurosawa, G. Nakane | |
| A robust coefficient of determination with the gamma divergence | |
| A1281: V. Zamanifarizhandi, J. Virta | |
| Oja depth for object data | |
| A1498: B. Nielsen, V. Berenguer Rico | |
| Least trimmed squares: Nuisance parameter free asymptotics | |
| A1335: T. Nakagawa, S. Kazari, K. Tahata, Y. Tsuruta | |
| Robust generalized Bayesian inference via divergences for von Mises-Fisher distribution |
| Session CC462 | Room: S0.03 |
| High-dimensional statistics and econometrics | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Andrew Wood | Organizer: CFE-CMStatistics |
| A0386: V. Avagyan | |
| Precision matrix estimation using penalized generalized Sylvester matrix equation | |
| A1656: S. Albalawi, R. Drikvandi | |
| On influential variables driving change points in high dimensional data | |
| A0336: S. Tanaka, H. Matsui | |
| Interaction screening via Kendall's rank correlation for imbalanced multi-class classification | |
| A1382: A. Drexel | |
| Challenges of cross-validation in post-double-Lasso: A Monte Carlo study | |
| A1565: R. Liesenfeld, G. Moura | |
| Regularized Wishart autoregressive stochastic volatility |
| Session CC439 | Room: S0.12 |
| Extreme values | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Abdelaati Daouia | Organizer: CFE-CMStatistics |
| A0884: M. Noori, M. Bee | |
| Asylum seekers at the extremes | |
| A1228: P. Osatohanmwen | |
| A new two-component hybrid model for highly right-skewed data sets with applications. | |
| A1313: F. Caeiro, I. Gomes | |
| Estimation of the extreme value index with probability weighted moments | |
| A1400: A. Mateus, F. Caeiro | |
| Estimation of the extreme value index using generalized probability weighted moments | |
| A1582: C. Cordeiro, D. Prata Gomes, C. Coelho, M. Neves | |
| Statistical analysis and modelling of extremes in time series |
| Session CC454 | Room: S0.13 |
| Biostatistics | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Federico Camerlenghi | Organizer: CFE-CMStatistics |
| A1259: O. Sahin | |
| Patient risk profiling with pair-copula constructions | |
| A1301: C. Chen, S. Das, M. Tisdall, F. Hu, A. Chen, P. Yushkevich, D. Wolk, R. Shinohara | |
| Subject-level segmentation precision weights for volumetric studies involving label fusion | |
| A1314: E. Derezea, H. Jones | |
| Network meta-analysis of diagnostic test accuracy reported at multiple thresholds | |
| A1392: J.L. Romero Bejar, J.M. Praena Fernandez, F.J. Esquivel | |
| Transcriptomics from the perspective of spatial statistics: Challenges and methodological approaches | |
| A1393: F.J. Esquivel, J.M. Praena Fernandez, J.L. Romero Bejar | |
| Multivariate techniques for high-dimensional analysis of genomic data |
| Session CC492 | Room: S-1.06 |
| Clustering | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Ioanna Papatsouma | Organizer: CFE-CMStatistics |
| A1234: S. Pal, C. Heumann | |
| Advancements in finite mixture models and flexible model-based clustering techniques | |
| A1542: E. Costa, I. Papatsouma, A. Markos | |
| A deterministic information bottleneck method for clustering mixed-type data | |
| A1580: M. Evangelou, E. Orme | |
| Non-negative matrix tri-factorization for multi-view biclustering | |
| A1527: N. Raveendran, G. Sofronov | |
| A hybrid approach for the spatial clustering problem via the cross-entropy method |
| Session CC425 | Room: S-1.27 |
| Multivariate methods | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Alessia Pini | Organizer: CFE-CMStatistics |
| A1282: M. Coblenz, B. Liu, O. Grothe | |
| The flow copula class | |
| A1357: F. Marques | |
| Testing the independence of variables for specific covariance structures: A simulation study | |
| A1049: S. Hermes, J. van Heerwaarden, P. Behrouzi | |
| Multi-attribute preferences: A transfer learning approach | |
| A1517: S. Yuki, K. Tanioka, H. Yadohisa | |
| Estimation methods of heterogeneous treatment effects extending the w-method and a-learner for multiple outcomes | |
| A1701: A. Acharyya, J. Arroyo, M. Clayton, M. Zlatic, Y. Park, C. Priebe | |
| Response prediction with convergence guarantees on multiple random graphs on unknown manifolds |
| Session CC459 | Room: S-2.25 |
| Nonparametric statistics | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Enea Bongiorno | Organizer: CFE-CMStatistics |
| A0926: S. Pereda-Fernandez | |
| Quantile regression with Bernstein polynomials | |
| A1315: H. Yamaguchi, H. Murakami | |
| Moment-generating function of the Hettmansperger-Norton-type test for patterned alternatives | |
| A1441: J.-C. Pardo-Fernandez, A. Fanjul Hevia, W. Gonzalez-Manteiga | |
| Tests for comparing ROC curves under the presence of covariates | |
| A1602: L. Amro, M. Pauly | |
| Resampling-based approaches for nonparametric MANOVA in the presence of missing data | |
| A1424: J. Bleher, L. Sartore | |
| Fast non-parametric test on the equivalence of multivariate empirical distributions | |
| A1730: S. Lee, I. Kim, S. Cha | |
| General frameworks for conditional two-Sample testing |
| Session CC443 | Room: BH (S) 2.05 |
| Asset pricing | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Alessandra Amendola | Organizer: CFE-CMStatistics |
| A1351: A. Stephan, M. Sahamkhadam, H. Loof, P. Dahlstrom | |
| Asset pricing of defining carbon emission targets | |
| A1442: M. Dauber | |
| 3D-PCA in foreign exchange markets | |
| A1526: L. Petrasek, J. Kukacka | |
| US equity announcement risk premia | |
| A1679: J. Royer, F. Ielpo | |
| Cross-asset value | |
| A1711: M. Kiermeier | |
| Wavelet analysis and the financial performance of ESG-Strategies |
| Session CC501 | Room: BH (SE) 2.01 |
| Advances in econometrics and financial modelling | Saturday 14.12.2024 10:05 - 12:10 |
| Chair: Masayuki Hirukawa | Organizer: CFE-CMStatistics |
| A1522: P. Jasko | |
| Random dynamical systems in a statistical arbitrage strategy on the stock market | |
| A1518: A. Majchrowska, S. Roszkowska | |
| Minimum wage and inflation in European Union countries | |
| A1708: K.-I. Inaba | |
| A global look into stock indices for dividend payout, corporate cash, and ESG issues | |
| A1710: I. Tsener, M. Kulish | |
| Piecewise linear solutions for non-stationary models | |
| A1714: B. Uniejewski | |
| A new approach to constructing probabilistic forecasts with smoothing quantile regression |
| Parallel session C: CFECMStatistics2024 | Saturday 14.12.2024 | 13:40 - 15:20 |
| Session CO131 | Room: K0.16 |
| Novel inference and modeling on network data and applications | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Wen Zhou | Organizer: Wen Zhou |
| A0166: K. Levin | |
| Asymptotic failure of peer effects in network regression models | |
| A0314: E.J. Zhang | |
| Preferential latent space models for networks with textual edges | |
| A0433: J. Cape, W. Jiang, J. Arroyo, C. McKennan | |
| Simultaneous estimation of connectivity and dimensionality in samples of networks | |
| A1162: L. Forastiere | |
| Regression discontinuity designs under interference |
| Session CO083 | Room: K0.18 |
| Statistical methods in weather forecasting I | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Bastien Francois | Organizer: Bastien Francois, Kirien Whan, Elisa Perrone |
| A1265: J. Wessel, C. Ferro, G. Evans, F. Kwasniok | |
| Improving probabilistic forecasts of extreme winds by training post-processing models with weighted scoring rules | |
| A1289: S. Baran, M. Leutbecher | |
| Fair logarithmic score for multivariate Gaussian forecasts | |
| A1337: K. Klein, S. Dirksen, M. Schmeits, K. Whan | |
| Probabilistic post-processing of wind speed forecasts with explicit modeling of time dependencies | |
| A1373: B. Francois, H. Kivril, M. Schmeits, K. Whan, P. Naveau | |
| Incorporating climatological constraints into statistical models to improve the post-processing of extremes |
| Session CO410 | Room: K0.19 |
| Statistical and computational methods for longitudinal and survival data | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Panpan Zhang | Organizer: Panpan Zhang |
| A0714: S. Banerjee | |
| Bayesian modelling and inference for finite populations from process-based superpopulations | |
| A0731: K.C.G. Chan, S. Wilkins-Reeves, Y.-C. Chen | |
| Data harmonization via regularized nonparametric mixing distribution estimation | |
| A0957: S. Xie, Y. Shi | |
| Cox regression model with auxiliary endpoints accounting for left truncation, complex censoring, and missing data | |
| A1062: D. Liu | |
| Predictive partly conditional model for longitudinal outcomes in the presence of informative dropout and death |
| Session CO117 | Room: K0.20 |
| Distance based methods in model specification testing and selection | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Bojana Milosevic | Organizer: Bojana Milosevic |
| A0388: M.D. Jimenez-Gamero, V. Alba-Fernandez, F. Jimenez-Jimenez | |
| Comparing mixing data | |
| A1420: M. Matsui | |
| Distance covariance for random fields | |
| A1272: W. Ning | |
| Sequential change-point detection for skew normal distribution | |
| A1091: L. Kunkel, M. Trabs | |
| A Wasserstein perspective of Vanilla GANs |
| Session CO270 | Room: K0.50 |
| Design and analysis of complex experiments (virtual) | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: MingHung Kao | Organizer: MingHung Kao |
| A1192: W. Zheng, Z. Zhou, R. Mee, H. Hamers | |
| Fast approximation of Shapley values through fractional factorial designs | |
| A0461: H.-L. Hsu | |
| Optimal designs with multiple correlated responses for experiments with mixtures | |
| A0462: J.-W. Huang, Y.-H. Chen, F.K.H. Phoa, Y.-H. Lin, S.P. Lin | |
| An efficient approach for identifying important biomarkers for biomedical diagnosis | |
| A0729: M. Kao | |
| Optimal next stage designs for sparse longitudinal data |
| Session CO228 | Room: K2.40 |
| High-dimensional time series and data integration | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Vladas Pipiras | Organizer: Vladas Pipiras |
| A0649: M. Duker | |
| Discovering common structures across high-dimensional factor models | |
| A0723: J. Hannig, Q. Tran-Dinh, J. Prothero, A. Ackerman, M. Jiang, S. Marron | |
| Data integration via analysis of subspaces (DIVAS) | |
| A0848: Z. Fisher | |
| Characterizing heterogeneous dynamics in multiple-subject multivariate time series | |
| A0968: S. Roy | |
| A regularized low tubal-rank model for high-dimensional time series data |
| Session CO396 | Room: K2.41 |
| Survival analysis: Truncated data | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Takeshi Emura | Organizer: Takeshi Emura |
| A0208: R. Weissbach, E. Scholz | |
| Left-truncated durations: Theoretical review and empirical applications | |
| A0247: A.-M. Toparkus, R. Weissbach | |
| Testing truncation dependence and goodness-of fit for double-truncated durations | |
| A0892: C. Moreira, J. de Una-Alvarez | |
| Regression and prediction for competing risks with doubly truncated data | |
| A0858: J. de Una-Alvarez, J.C. Escanciano | |
| Goodness-of-fit testing with survival data |
| Session CO156 | Room: S0.03 |
| New advances on computationally efficient statistical inference (virtual) | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Chong Jin | Organizer: Zuofeng Shang |
| A1262: Z. Gao, X. Wang, X. Kang | |
| Ensemble LDA via the modified Cholesky decomposition | |
| A1452: Y. Luo | |
| Improving the prediction of polygenic risk score: Overcoming challenges toward precision medicine | |
| A1645: C. Jin, R. Liu, Q. Long | |
| Using tissue-specific genetic variation in Mendelian randomization | |
| A1096: S. Qiu | |
| Adaptive-TMLE for the average treatment effect based on randomized controlled trial augmented with real-world data |
| Session CO013 | Room: S0.11 |
| Advances in computational neuroscience | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Sharmistha Guha | Organizer: Sharmistha Guha |
| Session CO110 | Room: S0.12 |
| Machine learning methods in extremes | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Abdelaati Daouia | Organizer: Abdelaati Daouia |
| A0548: N. Gnecco, S. Engelke, E. Merga Terefe | |
| Extremal random forests | |
| A0746: D. Nkameni | |
| An optimal index insurance framework for extreme losses | |
| A0912: G. Buritica, S. Engelke | |
| Approximation principle for domain generalization | |
| A1242: T. Staud, A. Buecher | |
| Bootstrapping block maxima estimators |
| Session CO125 | Room: S0.13 |
| Recent advances in biostatistics | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Yuedong Wang | Organizer: Yuedong Wang |
| A0265: T. Tong | |
| An alternative measure for quantifying the heterogeneity in meta-analysis | |
| A1008: C. Wang, K. Roeder, L. Wasserman | |
| Estimating causal effects with proximal inference methods in single-cell CRISPR screens | |
| A1352: Y. Li | |
| Penalized deep partially linear Cox models | |
| A1456: W. Guo | |
| Time-to-event analysis with unknown time origins via longitudinal biomarker registration |
| Session CO328 | Room: S-1.01 |
| HiTEc: Theory and applications in functional statistics | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Enea Bongiorno | Organizer: Enea Bongiorno |
| A0707: M.L. Battagliola, M. Bladt | |
| Extremile scalar-on-function regression with application to climate scenarios | |
| A0572: A. Coin, J. Berrendero, A. Cuevas | |
| A RKHS-based Bayesian approach to functional regression | |
| A0799: N. Bourarach, V. Rivoirard, A. Roche, F. Picard | |
| Minimax estimation for FPCA on discretized data | |
| A1094: M. Vidal, A.M. Aguilera | |
| Some properties of ICA in infinite-dimensional settings |
| Session CO140 | Room: S-1.04 |
| Branching and related processes I | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Miguel Gonzalez Velasco | Organizer: Ines M del Puerto, Miguel Gonzalez Velasco |
| A1521: M. Slavtchova-Bojkova, P. Mayster | |
| Markov branching processes with infinite mean immigration | |
| A1431: F. Palacios Rodriguez, A. Gomez Corral, M. Lopez Garcia | |
| Continuous-time Markov chain models with time-dependent rates | |
| A1669: F. Deng, A. Vidyashankar | |
| Sharp large deviations for branching process with immigration | |
| A1577: I.M. del Puerto, M. Gonzalez Velasco, A. Vidyashankar, C. Minuesa | |
| On Bayesian estimation via divergences for controlled branching processes |
| Session CO239 | Room: S-1.06 |
| Recent advances in high-dimensional statistical learning | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Tianying Wang | Organizer: Tianying Wang |
| Session CO317 | Room: S-1.27 |
| Selected topics in statistical machine learning | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Katherine Thompson | Organizer: Tapabrata Maiti |
| A0624: G. Vinci | |
| Dependence structure estimation from incomplete data | |
| A0843: R. Guhaniyogi, A. Scheffler | |
| Exploring Bayesian learning with heterogeneous image sources: From non-parametric models to deep learning architectures | |
| A1139: K. Thompson | |
| Machine learning methods: Probability of correct model selection using $R^2$ or AIC | |
| A1605: A. Samaddar, V. Nilsson, P. Nyquist, S. Madireddy | |
| A Corrective Transformations for Improved Neural Entropy Estimation |
| Session CO261 | Room: S-2.25 |
| Copulas: Methodology and applications | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Radu Craiu | Organizer: Radu Craiu |
| A0169: M. Hofert | |
| Index-mixed copulas | |
| A0456: B. Nasri, P. Krupskiy, B. Remillard | |
| On factor copula-based mixed regression models | |
| A0470: C. Grazian, F. Chen, H. Xuan | |
| Approximate Bayesian computation for factor copula models | |
| A1051: R. Craiu, R. Zimmerman | |
| Latent variable models with copulas |
| Session CO104 | Room: Auditorium |
| Non-Gaussian and noncausal time series | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Joann Jasiak | Organizer: Joann Jasiak |
| A0177: Y. Lu, J. Pei | |
| Mixed causal-noncausal count process | |
| A0379: R. Halbleib, L. Schmidt-Engelbertz | |
| Extracting efficient prices using intrinsic time information | |
| A0429: A. Manafi Neyazi, J. Jasiak | |
| GCov-based Portmanteau test | |
| A0451: A. Thomas, G. De Truchis, S. Fries | |
| Forecasting extreme trajectories using semi-norm representations |
| Session CO372 | Room: BH (S) 2.01 |
| Financial econometrics and machine learning | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Xiaohan Xue | Organizer: Xiaohan Xue, Shifan Yu |
| A1119: C. Wang, M.-N. Tran, R. Gerlach, R. Kohn | |
| Deep learning enhanced financial time series forecasting | |
| A1196: J. Zheng, P. Zhang, R. Harris | |
| GNN based social medium analysis in stock prediction | |
| A1488: X. Xue | |
| The likelihood ratio test for changes in high-dimensional idiosyncratic network | |
| A1646: X. Meng, Y. Lu, M. Mailhot, J.A. de Ita Solis | |
| Backtesting expectile: Disentangling unconditional coverage and independence properties |
| Session CO385 | Room: BH (S) 2.02 |
| Heterogeneity in panel data models | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Jiaying Gu | Organizer: Jiaying Gu |
| A1372: X. Bei | |
| Inference on union bounds | |
| A1388: C. Gaillac | |
| Predicting unobserved individual-level causal effects | |
| A1471: J. Tao, J. Gu, S. Volgushev | |
| C(alpha) test for number of components in finite mixture models | |
| A1536: J. Gu | |
| Identification of dynamic panel logit models with fixed effects |
| Session CO175 | Room: BH (S) 2.03 |
| Quantitative climate analysis | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Jesus Gonzalo | Organizer: Jesus Gonzalo |
| A0185: L. Gadea, J. Gonzalo | |
| Global and regional long-term climate forecasts: A heterogeneous future | |
| A0199: A. Ramos | |
| An unconditional-quantile vector error correction model to analyze climate heterogeneity | |
| A0281: J. Olmo, J. Gonzalo, L. Gadea | |
| Testing extreme warming and geographical heterogeneity | |
| A1491: S. Sanchez Alegre, H. Seoane | |
| The real effects of climate volatility shocks |
| Session CO010 | Room: BH (S) 2.05 |
| Topics in finite sample econometrics | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Antoine Djogbenou | Organizer: Antoine Djogbenou |
| A0225: L. Bauer | |
| Evaluating financial tail risk forecasts with the model confidence set | |
| A0973: P. Rilstone | |
| Higher-order moments of GMM estimators | |
| A0584: R. Moussa | |
| Treatment effects for Interval valued data: A copula-based analysis | |
| A0842: P. Tuvaandorj | |
| Permutation tests for dyadic models |
| Session CO064 | Room: BH (SE) 1.01 |
| Advances in high-dimensional Bayesian inference | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: David Rossell | Organizer: David Rossell |
| A0206: R. Casarin, A. Peruzzi | |
| Efficient Gibbs sampling for latent space models | |
| A0600: D. Telesca | |
| Bayesian transfer learning with multiple auxiliary datasets | |
| A0809: A. Simoni, M. Mogliani, L. Rossini | |
| The importance of the tails in high dimensional macroeconomic forecasting | |
| A0891: P. Rognon-Vael, D. Rossell | |
| External information for high-dimensional variable selection |
| Session CO121 | Room: BH (SE) 1.02 |
| Modern challenges in Bayesian nonparametric inference | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Federico Camerlenghi | Organizer: Mario Beraha, Federico Camerlenghi |
| A0450: F. Stolf, A. Canale | |
| Bayesian adaptive Tucker decompositions for tensor factorization | |
| A0468: F. Leisen, C. Villa, K. Wilson, F. Serafini | |
| Loss-based prior for tree topologies in BART models | |
| A0476: R. Passeggeri | |
| Random signed measures | |
| A0785: M. De Iorio | |
| Latent random partition model: An application to childhood co-morbidity |
| Session CO168 | Room: BH (SE) 1.05 |
| Recent developments in financial modelling and forecasting | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Spyros Vrontos | Organizer: Ekaterini Panopoulou |
| A1047: T. Pantelidis, M. Karantali, T. Panagiotidis | |
| Convergence in academic productivity | |
| A1071: S. Vrontos | |
| Pairs trading using machine learning models | |
| A1072: S. Alsaed, S. Vrontos | |
| Enhanced covariance matrix estimators in portfolio management: Comparing parametric and nonparametric approaches | |
| A1661: F. Javed, K. Mansson, D. Dai, P. Karlsson | |
| Nonlinear forecasting with many predictors using mixed data sampling kernel ridge regression models |
| Session CO008 | Room: BH (SE) 1.06 |
| Theory and implementation of statistics for stochastic processes | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Hiroki Masuda | Organizer: Hiroki Masuda |
| A0344: M. Uchida, Y. Tonaki, Y. Kaino | |
| Parameter estimation for linear parabolic SPDEs in two space dimensions based on high frequency spatio-temporal data | |
| A0364: L. Mercuri, H. Masuda | |
| Locally stable approximation of SDE: Numerical algorithms in yuimaStable | |
| A0420: J. Soehl, L. Koorevaar, S. Tendijck | |
| Spectral calibration of time-inhomogeneous exponential Levy models | |
| A0469: Y. Koike | |
| A note on uniform confidence bands for spot volatility |
| Session CO409 | Room: BH (SE) 2.01 |
| Advances in clustering and its application areas | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Ioanna Papatsouma | Organizer: Ioanna Papatsouma, Marina Evangelou |
| A0324: C. Hennig | |
| On decision making in cluster analysis | |
| A0654: S. Coleman | |
| Bayesian clustering of complex data | |
| A0827: C. Biernacki, C. Keribin, J. Jacques | |
| Model-based co-clustering: High dimension and estimation challenges | |
| A1032: M. Markatou | |
| Weighting games in clustering |
| Session CO250 | Room: BH (SE) 2.05 |
| Statistical methods for network psychometrics | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Federico Castelletti | Organizer: Federico Castelletti |
| A0804: A. Mascaro, F. Castelletti, A. Fasano | |
| A DAG-probit model for Bayesian causal inference and causal structure learning from ordinal data | |
| A0914: M. Marsman | |
| Comparing ordinal Markov random fields in two independent samples with Bayesian model selection | |
| A0985: R. Mohammadi | |
| Large-scale Bayesian structure learning for Gaussian graphical models using marginal pseudo-likelihood | |
| A0750: M. Zambelli | |
| Application of the meta-analytic Gaussian network aggregation approach to investigate flourishing across 22 countries |
| A0495: I. Sulis, S. Columbu, M. Porcu, C. Usala | |
| Fostering resilience at the university: Statistical models for assessing the influence of high schools and peers | |
| A0533: R. De Santis, N. Salvati, F. Schirripa, A. D Agostino | |
| Studying gender disparities in STEM university credits distribution using quantile regression | |
| A0627: S. Bacci, B. Bertaccini, L. Scaffidi Domianello | |
| Clustering of Italian higher education institutions based on the mobility choices of academic students | |
| A1035: M. Zenga, A. Marshall | |
| The mathematics performance among adolescents in the COVID-19 era |
| Session CO302 | Room: BH (SE) 2.12 |
| Spatiotemporal infectious disease modeling and surveillance | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Chawarat Rotejanaprasert | Organizer: Chawarat Rotejanaprasert |
| A0712: A. Lawson | |
| Multi-scale spatiotemporal Covid-19 modeling: The mortality example | |
| A1108: J. M Mendes | |
| Breaking down homogeneous mixing assumptions in epidemic compartmental models | |
| A1158: R. Deardon | |
| Directionally dependent spatial infectious disease models | |
| A1174: H. Baptista, J. M Mendes, Y.C. MacNab | |
| A new similarity-based spatiotemporal model for Covid-19 infection prediction and forecasting |
| Session CO178 | Room: Safra Lec. Theatre |
| Methods for analyzing structured data | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Israel Almodovar Rivera | Organizer: Israel Almodovar Rivera |
| A0257: F. Dai, K. Dorman, S. Dutta, R. Maitra | |
| Exploratory factor analysis of data on a sphere | |
| A0268: A. Rodriguez | |
| On data analysis pipelines and modular Bayesian modeling | |
| A0967: K. Dorman | |
| New methods to genotype allopolyploids | |
| A0971: R. Maitra, C. Llosa-Vite | |
| Gauge reproducibility and repeatability for matrix-variate data with application to forensic fracture surface-matching |
| Session CO080 | Room: K2.31 (Nash Lec. Theatre) |
| Advances in causal inference | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Luke Keele | Organizer: Luke Keele |
| A0423: L. Keele | |
| Local instrumental variable curves without positivity assumptions | |
| A0498: D. Knox, L. Keele, G. Duarte, K. Cooper, J. Mummolo, K. Mattes | |
| Evaluating the validity and robustness of instrumental-variable analyses | |
| A0540: J. Zubizarreta, P. Rosenbaum | |
| Effect aliasing in observational studies | |
| A1061: S. Seaman, R. Keogh | |
| Simulating data from marginal structural models for a survival time outcome |
| Session CO026 | Room: BH (S) 1.01 Lec. Theathre 1 |
| Topics in time series econometrics | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Johan Lyhagen | Organizer: Johan Lyhagen |
| A0665: J. Andersson | |
| Statistical modelling of variables on the unit interval (the closed interval between zero and one) | |
| A0753: Y. Li | |
| An extension of likelihood ratio based tests for evaluating the interval forecast | |
| A0933: P. Karlsson, S. Muhinyuza, M. Sahamkhadam | |
| Beta regression: Shrinkage-Liu type estimator with application | |
| A0943: Y. Yang | |
| Robust LM-type testing in multivariate contaminated time series |
| Session CC499 | Room: BH (SE) 2.09 |
| Econometric models in energy and finance | Saturday 14.12.2024 13:40 - 15:20 |
| Chair: Joachim Schnurbus | Organizer: CFE-CMStatistics |
| A1621: E. Caro Huertas, J. Juan | |
| Analyzing the impact of photovoltaic self-consumption on Spanish electricity consumption patterns | |
| A1525: F. Diaz-Rodriguez, M.D. Robles | |
| 'One out of many': Consolidating a long-term trend forecast for investing in energy commodities | |
| A0960: R. Huptas | |
| Forecasting of intraday trading volume using Bayesian nonlinear ACV models for a VWAP trading strategy |
| Parallel session E: CFECMStatistics2024 | Saturday 14.12.2024 | 16:50 - 18:55 |
| Session CO379 | Room: K0.16 |
| Advances in networks and causal inference | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Michael Schweinberger | Organizer: Michael Schweinberger |
| A1129: J. Stewart | |
| Rates of convergence and normal approximations for estimators of local dependence random graph models | |
| A1133: M. Tang, J. Cape | |
| Statistical inference and eigenvector fluctuations for random graphs with infinite rank kernels | |
| A0496: C. Fritz, M. Schweinberger, D. Hunter, S. Bhadra | |
| A regression framework for studying relationships among attributes under network interference | |
| A1085: S. Bhadra, M. Schweinberger, V. Karwa | |
| Causal inference under interference with dependent outcomes due to treatment and outcome spillover | |
| A1670: O.H. Madrid Padilla | |
| Network two-sample test for block models |
| Session CO358 | Room: K0.18 |
| Statistical methods in weather forecasting II | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Sandor Baran | Organizer: Sandor Baran |
| A0673: D. Jobst, A. Moeller, J. Gross | |
| Gradient-boosted conditional vine copula models for multivariate temperature forecasting | |
| A0686: A. Moeller, D. Jobst, J. Gross | |
| D-vine copula based probabilistic weather forecasting | |
| A0733: J. Gross, A. Moeller | |
| The zero degree of freedom non-central chi squared distribution for ensemble postprocessing | |
| A0783: M. Nagy-Lakatos, S. Baran | |
| Enhancing multivariate post-processed visibility predictions utilizing CAMS forecasts | |
| A0865: S. Allen, X. Shen, J. Ziegel | |
| Statistical post-processing of weather forecasts using engression |
| Session CO194 | Room: K0.19 |
| Statistical challenges in interdisciplinary biomedical research | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Sarah Weinstein | Organizer: Sarah Weinstein |
| A0536: D. Isenberg, M. Harhay, F. Li, N. Mitra | |
| Bayesian estimation of the survivor average causal effect for cluster-randomized crossover trials | |
| A0631: N. Illenberger | |
| Zero-inflated latent class mixed models for characterizing longitudinal engagement patterns | |
| A0901: B. Ren | |
| From Poisson to Bernoulli: Unlocking the finite sample properties of survival processes | |
| A0930: R. Deek | |
| Joint estimation and false discovery control of causal effects in metabolomics randomized trials | |
| A1559: D. Tu | |
| Digital biomarkers of Parkinson's disease using free-living accelerometry data |
| Session CO093 | Room: K0.20 |
| Model assessment | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Maria Dolores Jimenez-Gamero | Organizer: Maria Dolores Jimenez-Gamero |
| A0601: P. Wuebbolding, D. Gaigall | |
| A goodness-of-fit test for geometric Brownian motion | |
| A0688: D. Gaigall, L. Baringhaus | |
| A goodness-of-fit test for the geometric maximum compound logistic distribution model | |
| A0899: B. Milosevic, J. Radojevic | |
| From independence tests to variable selection problems: Moving beyond Euclidean settings | |
| A0961: A. Lago, J.-C. Pardo-Fernandez, J. de Una-Alvarez | |
| Tests for left-truncated and right-censored data | |
| A0328: M.R. Sillero-Denamiel, M.D. Jimenez-Gamero | |
| The k-sample problem using Gini covariance for large k |
| Session CO148 | Room: K0.50 |
| Experimental design: Screening experiments | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: John Stufken | Organizer: John Stufken |
| A0256: B. Smucker, S. Wright, I. Williams, R. Page, A. Kiss, S. Bikram Silwal, M. Weese, D. Edwards | |
| Row-constrained supersaturated designs for high-throughput screening | |
| A0834: K. Young | |
| A Supersaturated screening design framework based on lasso support recovery | |
| A1188: C. Nachtsheim, B. Jones, R. Lekivetz, D. Majumdar | |
| Two new classes of mixed-level screening designs inspired by definitive screening designs | |
| A1088: U. Groemping | |
| Implementing arrays for fault detection in R software |
| Session CO375 | Room: K2.40 |
| Recent approaches to environmental and spatio-temporal statistics | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: William Kleiber | Organizer: Stefano Castruccio |
| A0647: M. Sainsbury-Dale, A. Zammit Mangion, J. Richards, R. Huser | |
| Neural methods for likelihood-free inference in spatial and spatiotemporal models | |
| A0715: H. Sun, Y. Chen | |
| Uncertainty quantification of spatiotemporal tensor completion | |
| A0789: L. De Monte, I. Papastathopoulos, R. Campbell, H. Rue | |
| A geometric approach to extreme value theory with application to flood risk modelling | |
| A0879: L. Llamazares, F. Lindgren, J. Latz | |
| Anisotropic Gaussian random fields with identifiable parameters and penalized-complexity priors | |
| A1676: V. Berrocal | |
| Bayesian source apportionment of PM2.5 species data collected over space and time |
| Session CO408 | Room: K2.41 |
| Modern statistical inferential methods for complex data analysis | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Zhe Fei | Organizer: Shujie Ma |
| Session CO089 | Room: S0.11 |
| Advances in data integration for large-scale observational studies | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Andrew Chen | Organizer: Andrew Chen |
| A0294: R. Shinohara | |
| Modern neuroimaging data harmonization methods for multi-center studies | |
| A0448: J.Y. Park | |
| Promises of covariance harmonization in multi-site neuroimaging studies | |
| A0700: D. Tudorascu | |
| Integration of PET imaging studies in Alzheimer's disease | |
| A0763: J. Choi, R. Sun | |
| Bayesian variable selection for interval-censored outcomes in Genome-wide association studies | |
| A0991: E. Johnson | |
| Methods for robust multi-study genomic data integration: Applications in infectious diseases research |
| Session CO115 | Room: S0.12 |
| Copula and extreme dependence | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Giorgia Rivieccio | Organizer: Giorgia Rivieccio |
| A0596: U. Can, R. Laeven, J. Einmahl | |
| Two-sample testing for tail copulas with an application to equity indices | |
| A0621: V. Candila, A. Naimoli | |
| Impact of exogenous factors on tail risk measures in Australian electricity markets | |
| A0699: O. Ardakani | |
| Adaptive thresholding and tail index estimation under normal and extreme regimes | |
| A0906: M.M. Ippolito, G. De Luca, G. Rivieccio | |
| US banking returns and copper global price: A DCC-GARCH-MIDAS approach | |
| A0932: C. Yang | |
| Tail maximal dependence in bivariate models: Estimation and applications | |
| A0947: A. Montanino, G. De Luca | |
| A mixture copula model for assessing net bubble values risk |
| Session CO325 | Room: S0.13 |
| Bayesian and computational methods for better healthcare decisions | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Fan Bu | Organizer: Fan Bu |
| A0425: V. Ballerini, A. Mattei, F. Mealli | |
| Principal stratum strategy for safety evaluation | |
| A0520: I. Chen | |
| Bayesian hierarchical methods for modeling individual level variances for predicting health outcomes | |
| A0738: F. Denti | |
| Spatial nested mixture models for MALDI-MSI image segmentation | |
| A0765: S. Chattopadhyay, M. Suchard | |
| Hamiltonian Monte Carlo for Bayesian nonparametric clustering via soft multinomial approximations | |
| A1427: F. Shokoohi | |
| Statistical learning in high-dimensional methylation data in cancer using trans-dimensional hidden Markov models |
| Session CO041 | Room: S-1.01 |
| HiTEc: Clustering of complex data structures | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Maria Brigida Ferraro | Organizer: Maria Brigida Ferraro |
| A0922: D. Failli, M.F. Marino, F. Martella | |
| Mixture of generalized latent trait analyzers for jointly clustering pediatric patients and their clinical conditions | |
| A1018: P. McNicholas, K. Clark | |
| Handling outliers when clustering three-way data | |
| A1376: A.B. Ramos-Guajardo, M.B. Ferraro, G. Gonzalez-Rodriguez, J. Grana Colubi | |
| Fuzzy clustering approaches for star-shaped sets: A comparative study | |
| A1585: M. Neal, P. McNicholas | |
| A comparison of parsimonious families of hidden Markov models for multivariate longitudinal data | |
| A1271: G.M. Sangiovanni, L. Kontoghiorghes, A. Colubi, M.B. Ferraro | |
| Hypothesis test based document clustering |
| Session CO141 | Room: S-1.04 |
| Branching and related processes II | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Ines M del Puerto | Organizer: Ines M del Puerto, Miguel Gonzalez Velasco |
| A1675: A. Vidyashankar, G. Francisci | |
| Limit laws for tree-indexed autoregression | |
| A0248: C. Gutierrez Perez, C. Minuesa Abril | |
| Modelling predator-prey systems with two-sex branching processes | |
| A1437: E. Yarovaya | |
| Spectral methods and their application in stochastic analysis | |
| A1182: S. Johnston | |
| Ancestral reproductive bias in branching processes | |
| A1524: M. Gonzalez Velasco, P. Martin-Chavez, I.M. del Puerto | |
| Asymptotic behavior of critical multitype branching processes with random migration |
| Session CO165 | Room: S-1.06 |
| Over-parametrization and overfitting in machine learning | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Debarghya Ghoshdastidar | Organizer: Debarghya Ghoshdastidar |
| A1001: R. Burkholz | |
| Deep learning at smaller scale | |
| A0934: T. Viering | |
| Surprising learning curves: More data can lead to worse performance and worse estimators | |
| A0418: L. Chennuru Vankadara | |
| Theoretical foundations of scaling | |
| A0374: P. Esser, G. Anil, D. Ghoshdastidar | |
| When can we approximate wide contrastive models with neural tangent kernels and principal component analysis | |
| A0963: R. Sonthalia | |
| Double descent and benign overfitting for error in variables regression |
| Session CO101 | Room: S-1.27 |
| Resampling methods in modern settings | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Miles Lopes | Organizer: Miles Lopes |
| A0279: F. Xie | |
| Edgeworth expansion and bootstrap for entrywise eigenvectors statistics of low-rank random matrices | |
| A0282: X. Shao | |
| Change-point inference for high-dimensional heteroscedastic data | |
| A0350: C.M. Le, Z. Shao | |
| Parametric bootstrap on networks with non-exchangeable nodes | |
| A1031: D. Nordman, K. Gregory | |
| Bootstrap inference for least angle regression |
| Session CO341 | Room: S-2.25 |
| Directional statistics | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Anahita Nodehi | Organizer: Anahita Nodehi |
| A0585: M. Maadooliat | |
| Nonparametric collective (spectral) density estimation with applications in Bioinformatics | |
| A0628: S. Loizidou, C. Ley, S. Kato, K. Mardia | |
| A versatile trivariate wrapped Cauchy copula | |
| A0803: M. Geraci | |
| Generalized Laplace regression to model cylindrical responses with an application to physical activity in children | |
| A0966: M. Di Marzio, S. Fensore, C. Passamonti | |
| Addressing boundary inefficiency of local density estimators | |
| A0970: A. Panzera, A. Gottard | |
| Gaussian-related graphical models for circular variables |
| Session CO301 | Room: Auditorium |
| New developments in financial econometrics | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Roxana Halbleib | Organizer: Roxana Halbleib |
| A0176: J. Jasiak | |
| Nonlinear fore(back)casting and innovation filtering for causal-noncausal VAR models | |
| A0210: C. Sattarhoff | |
| Financial market efficiency during crisis periods: A long-memory approach based on price ranges | |
| A0634: E. Kazak, L. Bauer | |
| Forecast evaluation of financial tail risk: Conditional MCS | |
| A0269: C. Gourieroux, J. Jasiak | |
| Structural modelling of dynamic networks and identifying maximum likelihood | |
| A1248: R. Luger, X. Liu | |
| Quantile-based modeling of scale dynamics in financial returns for value-at-risk and expected shortfall forecasting |
| Session CO172 | Room: BH (S) 2.01 |
| Foundations of machine learning for economics and finance | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Artem Prokhorov | Organizer: Artem Prokhorov |
| A0998: D. Radojicic | |
| Evaluating the statistical characteristics and analyzing the ML models of the limit order book | |
| A0273: A. Spector, E. Candes, R. Foygel Barber, T. Hastie, R. Kahn | |
| The mosaic permutation test: An exact and nonparametric goodness-of-fit test for factor models | |
| A1655: D. Liu | |
| Sparse sieve MLE | |
| A1135: V. de la Pena | |
| The price of independence in a model with unknown dependence | |
| A1654: A. Prokhorov | |
| Improved semi-parametric bounds for tail probability and expected loss: Theory and applications |
| Session CO234 | Room: BH (S) 2.02 |
| Advanced statistical techniques: From risk to AI and beyond | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Silvia Montagna | Organizer: Silvia Montagna |
| A1666: A. Khorrami Chokami, G. Rabitti | |
| A copula-based data augmentation strategy for the sensitivity analysis of extreme operational losses | |
| A1649: R. Ascari, A. Giampino, S. Migliorati | |
| Identifying microbiome communities and enterotypes using a novel mixed-membership model | |
| A1639: D. Bracale, Y. Sun, M. Banerjee, S. Maity | |
| Learning the distribution map in reverse causal performative prediction | |
| A1671: C. Berloco, E. Capuano | |
| Enhancing embedding models through specialized fine-tuning in the banking sector | |
| A1672: E. Capuano, C. Berloco | |
| Displaying the performance-consumption tradeoff for aware and sustainable AI |
| Session CO311 | Room: BH (S) 2.03 |
| Modeling and measuring multivariate volatility and risk | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Ilya Archakov | Organizer: Ilya Archakov |
| A0786: Y. Chen, N. Hautsch, B. Peng, I. Bomze | |
| Cardinality constraints meet large-scale portfolio | |
| A0679: J. Rennspies, I. Archakov, R. Halbleib | |
| Dynamic factor model for realized covariance matrices | |
| A1168: Q. Lee, C. Gourieroux | |
| Forecast relative error decomposition | |
| A1308: A. Dufays, J. Rombouts, K. Jacobs | |
| Online forecasting of unbalanced implied volatility surfaces | |
| A1397: J. Leymarie | |
| Estimation risk for systemic risk measures driven by semi-parametric models |
| Session CO142 | Room: BH (SE) 1.01 |
| Bayesian statistics | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Dimitri Konen | Organizer: Dimitri Konen |
| A1132: G. Romer | |
| Valid uncertainty quantification for linear functionals in semi-parametric regression models | |
| A1065: F. Pozza | |
| Skewed Bernstein-von Mises theorem and skew-modal approximations | |
| A1100: S. Rizzelli | |
| Semiparametric empirical Bayesian analysis of maxima and peaks over threshold | |
| A1123: A. Pengel | |
| Output analysis for high-dimensional MCMC | |
| A1146: F. Seizilles, R. Nickl | |
| Bayesian inference for killed reflected diffusions |
| Session CO046 | Room: BH (SE) 1.02 |
| Topics in Bayesian modeling and computation | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Luca Maestrini | Organizer: Luca Maestrini |
| A0983: M. Lin, G. Livieri, L. Maestrini, M. Bernardi | |
| Probabilistic activation functions and semiparametric mean field variational learning in Bayesian neural networks | |
| A0773: G. Livieri, M. Bernardi, L. Maestrini | |
| A variational inference approach to variable selection for heteroskedastic regression models | |
| A0798: C. Castiglione | |
| Time-dependent stochastic block models with application to causes of death networks | |
| A0952: M. Bernardi, M. Cattelan, C. Busatto | |
| Fast Bayesian model selection algorithms for linear regression models | |
| A0292: A. Sheinkman, S. Wade | |
| Variational Bayesian Bow tie neural networks with shrinkage |
| Session CO189 | Room: BH (SE) 1.05 |
| Financial modeling: Regime switching and statistical learning | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Christina Erlwein-Sayer | Organizer: Christina Erlwein-Sayer |
| A0841: R. Mamon | |
| Stock market responses to climate risks: Sectoral-level evidence from the U.S. | |
| A1012: M. Phan, J. Sass, C. Erlwein-Sayer | |
| Modelling of financial time series with a regime-switching GARCH model including jumps | |
| A1014: C. Erlwein-Sayer, A. Rosenswie, A. Petukhina, S. Kepezkaya | |
| Sentiment analysis in an LSTM deep learning approach for forecasting volume in fractional trading | |
| A1326: S. Alkhoury | |
| Valuing real estate portfolios with machine learning using geospatial and macroeconomic data | |
| A1631: V. Bolovaneanu, D.T. Pele | |
| Bayesian Bandit portfolio: Customized Thompson sampling for investor preference |
| Session CO068 | Room: BH (SE) 1.06 |
| Recent developments in theoretical statistics | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Yue Zhao | Organizer: Marten Wegkamp |
| A0191: D. Yang | |
| On counting communities and finding them | |
| A0381: Y. Zhao | |
| Statistical properties of a flexible regularized estimating equation formulation | |
| A1519: X. Bing | |
| Optimal vintage factor analysis with deflation varimax | |
| A1433: K. Knight | |
| Adaptive ridge regression and fractional degrees of freedom | |
| A1322: H. Nishimori, T. Matsuda | |
| On the attainment of the Wasserstein-Cramer-Rao lower bound and the location scale family |
| Session CO259 | Room: BH (SE) 2.09 |
| Empirical macro | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Michael Owyang | Organizer: Michael Owyang |
| A0491: A. Karadimitropoulou, K. Beck | |
| Lost in aggregation: European, country, sectoral, and regional factors driving the GVA fluctuations in Europe | |
| A0432: N. Francis | |
| Assessing liquidity constraints: Does credit matter for the permanent income hypothesis | |
| A0537: A. Guisinger, M. Owyang, M. McCracken | |
| The relative importance of news and knowledge | |
| A0808: A. Galvao | |
| Expectations vs data news on nowcasting US GDP | |
| A0812: M. Owyang, L. Coroneo, L. Jackson Young | |
| International stock co-movements and time-varying risks |
| Session CO161 | Room: BH (SE) 2.10 |
| Statistical models and methods for education II | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Antonella D Agostino | Organizer: Isabella Sulis, Antonella Dagostino |
| A0563: G. Boscaino, M. Vittorietti, O. Giambalvo | |
| A mediation analysis approach for gender pay gap in STEM: The university of Palermo case | |
| A0594: K. Breznik, G. Ragozini, M. Restaino, M.P. Vitale | |
| Identifying exchange patterns in Erasmus+ mobility | |
| A0597: M. Restaino, M. La Rocca, M. Niglio, M.P. Vitale | |
| Exploring factors affecting gender gap in university student performance | |
| A0598: M. Vittorietti, E. Musta | |
| A discrete-time mover and stayer model with time-varying covariates for studying Italian student mobility | |
| A1416: A. Kimoto, J. Tsuchida, H. Yadohisa | |
| Q-matrix estimation in cognitive diagnostic models by using overlapping clustering |
| Session CO304 | Room: BH (SE) 2.12 |
| Statistics in geosciences | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Radomyra Shevchenko | Organizer: Radomyra Shevchenko |
| A1227: J. Lilly | |
| Statistics and structures: Examples from oceanography | |
| A1306: A. Sykulski | |
| Machine learning and spatiotemporal statistics in the ocean: Fusing data sources and making nonlinear predictions | |
| A1137: S. Juricke | |
| Scale interactions in the ocean: Designing and analyzing stochastic turbulence closures in complex ocean models | |
| A1658: O. Lang, D. Crisan, A. Lobbe | |
| Generative modelling and stochastic parametrizations for a rotating shallow water system | |
| A1134: A. Vidotto, A. Caponera, D. Marinucci | |
| Multi-scale CUSUM tests for time dependent spherical random fields |
| Session CO391 | Room: Safra Lec. Theatre |
| Advances in functional and spatial data analysis | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Anna Calissano | Organizer: Eleonora Arnone |
| A1563: S. Kurtek, K. Bharath, X. Guo | |
| Variograms for Kriging and clustering of spatial functional data with phase variation | |
| A1618: H. Nassar | |
| Flexible functional data representation in higher dimensions using state space transformation | |
| A1408: T. Bortolotti, R. Troilo, A. Menafoglio, S. Vantini | |
| Flexible estimation of spatial covariance functions from multi-temporal DInSAR data | |
| A1409: J. Di Iorio | |
| A new motif discovery based method for forecasting and imputation of functional data | |
| A1615: B. Pulido Bravo, R. Lillo, A. Franco-Pereira | |
| Area-based epigraph and hypograph indices as a tool to detect outliers in functional data |
| Session CO257 | Room: K2.31 (Nash Lec. Theatre) |
| Statistics and sport | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Brigitte Gelein | Organizer: Brigitte Gelein |
| A0264: A. Damoulaki, I. Ntzoufras, K. Pelechrinis | |
| Lasso multinomial performance indicators for in-play basketball data | |
| A0280: N. Vergne, E.-N. Kalligeris, V.S. Barbu, G. Hacques, L. Seifert | |
| Drifting Markov models in learning of climbing | |
| A0535: A. Bouvet, M. Marbac, S. El Kolei | |
| Investigating swimming technical skills by a double partition clustering of multivariate functional data from IMU sensor | |
| A0657: M. Carlesso | |
| Optimizing game data: Efficient tagging and analytics for basketball | |
| A1371: D. Karlis, M. Oetting, R. Michels | |
| Statistical models for handball |
| Session CO349 | Room: BH (S) 1.01 Lec. Theathre 1 |
| Climate econometrics | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Helena Veiga | Organizer: Cristina Amado, Helena Veiga |
| A0424: R. Kruse-Becher | |
| Adaptive now- and forecasting of global temperatures under smooth structural changes | |
| A1130: M. Wiper, C. Ausin, A. Sarhadi | |
| Anthropogenic warming increases the risk of major tropical cyclones in a nonstationary climate | |
| A1415: G.P.E. Bellocca, P. Poncela, E. Ruiz | |
| Extreme temperatures and the profitability of large European firms | |
| A1461: T. Proietti, A. Giovannelli | |
| Climate normals and anomalies | |
| A1383: D. Li, V. Kitsios, D. Newth, T. OKane | |
| Machine learning projection of climate and technology impacts on crops key to food security |
| Session CC504 | Room: S0.03 |
| Statistical methods and applications I | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Catia Scricciolo | Organizer: CFE-CMStatistics |
| A1705: G. Tong, Y. Zeng, F. Li | |
| Propensity score weighting with complex survey data: Best practice | |
| A1709: Z. Liang, K. Mylona | |
| Statistical analysis of data from supersaturated split-plot experiments | |
| A1707: S. Zhou, L. Gabric, K. Zhou | |
| A Bayesian approach to discrimination-free insurance pricing with variational inference | |
| A1712: L. Cutillo, D. Righelli, V. Policastro, A. Carissimo | |
| Robustness in weighted networks | |
| A1120: F. Alqallaf | |
| Independent reads of Poisson flows |
| Session CC490 | Room: BH (S) 2.05 |
| MCMC methods and Bayesian computation | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Pavlo Mozharovskyi | Organizer: CFE-CMStatistics |
| A1538: K. Okada, K. Hijikata, M. Oka, K. Yamaguchi | |
| VariationalDCM 2.0: An updated R package for variational Bayesian inference in diagnostic classification models | |
| A1239: Y. Kuang, T. Kitagawa | |
| Identification-driven MCMC | |
| A1447: K. Heine | |
| Augmented island resampling particle filter for particle MCMC | |
| A1474: J.H. Tran, T. Selland Kleppe | |
| Tuning diagonal scale matrices for HMC | |
| A0195: G. Karabatsos | |
| Copula approximate Bayesian computation using distribution random forests |
| Session CC427 | Room: BH (SE) 2.01 |
| Time series | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Eliana Christou | Organizer: CFE-CMStatistics |
| A0306: S. Das, G. Qian, L. Zhang | |
| Periodogram regression a two stage mixed effects approach for tropical cyclone frequency | |
| A1200: G. Piscopo, E. di Lorenzo, A. Roviello, M. Sibillo | |
| Multi country analysis of the healthy life expectancy gap | |
| A1340: P. Galeano, D. Pena, R. Tsay | |
| Efficient outlier detection in heterogeneous time series databases | |
| A1435: I. Pereira, M. Monteiro, I. Pinto | |
| Count time series: Handling structural breaks | |
| A1462: N. Chakraborty, K. Khare, G. Michailidis | |
| Bayesian group-shrinkage based estimation for panel vector autoregressive models with mixed frequency data |
| Session CC482 | Room: BH (SE) 2.05 |
| Risk analysis | Saturday 14.12.2024 16:50 - 18:55 |
| Chair: Abdelaati Daouia | Organizer: CFE-CMStatistics |
| A1343: J. Perote, A. Mora Valencia, I. Jimenez | |
| Tail dependence in Gram-Charlier type multivariate distributions: The relevance of the moment spillovers | |
| A1349: C. Castro-Iragorri, F. Gomez | |
| Worst-case higher moment risk measures: Addressing procyclicality and stress-testing | |
| A1237: Z. Fan | |
| Dynamic shrinkage and selection for exchange rate forecasting | |
| A1245: A. Monteiro, R. Pascoal, M. Augusto | |
| Measuring default intensities through a reduced form credit risk approach |
| Parallel session F: CFECMStatistics2024 | Sunday 15.12.2024 | 08:45 - 10:25 |
| Session CI052 (Special Invited Session) | Room: Auditorium |
| Recent advances in clustering | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Matthieu Marbac | Organizer: Matthieu Marbac |
| A0187: G. d Angella, C. Hennig | |
| Choosing the number of biological species in the presence of spatial patterns of differentiation | |
| A0538: F. Martella, M. Ranalli | |
| Biclustering listeners and music genres using a composite likelihood-based approach | |
| A0217: Y. De Castro | |
| Mixture models via continuous sparse regression |
| Session CO095 | Room: K0.16 |
| New developments in statistical network analysis | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Jonathan Stewart | Organizer: Jonathan Stewart |
| A0570: S. Nandy, A. Chatterjee, R. Sadhu | |
| Detecting planted partition in sparse multi-layer networks | |
| A0708: J. Loyal, Y. Chen | |
| A spike-and-slab prior for dimension selection in generalized linear network eigenmodels | |
| A0764: J. Li, J. Stewart | |
| Learning cross-layer dependence structure in multilayer networks | |
| A1125: M. Schweinberger, C. Fritz, S. Bhadra, D. Hunter | |
| A regression framework for studying relationships among attributes under network interference: Statistical theory |
| Session CO222 | Room: K0.18 |
| Statistical methods for environmental sciences | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Julia Schaumburg | Organizer: Barend Spanjers, Julia Schaumburg |
| A0204: J. Gonzalo, L. Gadea, A. Ramos | |
| Trends in temperature data: Micro-foundations of their nature | |
| A0303: A.C. Cebrian, J. Castillo-Mateo, A. Gelfand, J. Asin, Z. Gracia | |
| Spatiotemporal modeling for record-breaking temperature events | |
| A0705: Y. Shapovalova | |
| Nowcasting of high precipitation events with deep learning | |
| A0791: B. Spanjers | |
| Modelling temperature persistence using seasonal quantile autoregressions |
| Session CO323 | Room: K0.19 |
| Bayesian methods for extreme values | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Ramses Mena | Organizer: Ramses Mena |
| A1140: C. Nava, R. Mena | |
| Construction, estimation and application of diffusion processes for extreme values | |
| A1224: P. Onorati, I. Antoniano-Villalobos, A. Canale | |
| Tuning-free objective Bayesian inference for extremes | |
| A1170: I. Antoniano-Villalobos, M. Zaman, I. Prosdocimi | |
| Enhancing intensity-duration-frequency curves estimation: A Markov dependence approach | |
| A1362: M. de Carvalho, D. Paulin | |
| A Bayesian Lasso for tail index regression |
| Session CO294 | Room: K0.20 |
| Distance and depth based methods for data science | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Silvia Salini | Organizer: Silvia Salini, Giancarlo Manzi |
| A1145: A. Grane Chavez, F. Scielzo | |
| Robust fast k-medoids for large mixed-type data | |
| A1152: E. Boj, A. Grane Chavez, A. Mayo-Iscar | |
| Robust distance-based generalized linear models: Some proposals | |
| A1380: G. Manzi, A. Grane Chavez, Q. Guo | |
| Converting textual data into structured survey data: A ChatGPT approach | |
| A1131: M. Ochoa, I. Cascos, H.T. Khanh Linh | |
| Computation of non-zero empirical expectile depths |
| Session CO087 | Room: K0.50 |
| Design of experiments for complex data | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Kalliopi Mylona | Organizer: Kalliopi Mylona |
| A0272: V. Chasiotis, L. Wang, D. Karlis | |
| Variable shrinkage and subdata selection in big data | |
| A0278: I. Garcia Camacha Gutierrez, K. Mylona | |
| I-optimal robust Bayesian designs to control model misspecification | |
| A0490: C. May | |
| Dynamic design of experiments for function-on-function linear models | |
| A0831: R. Mitra | |
| Optimized recovery sampling to test for missing not at random |
| Session CO352 | Room: K2.40 |
| High-dimensional statistics with nuisance parameters | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Tengyao Wang | Organizer: Tengyao Wang |
| A1296: Q. Li, W. Jin | |
| Inference on the significance of modalities in multimodal generalized linear models | |
| A1644: F. Gao | |
| Inference of transition time in steady-state variations in smFRET via a Wasserstein distance approach | |
| A1648: Y. Wang, K. Wen, T. Wang | |
| Residual permutation test for high-dimensional regression coefficient testing | |
| A1411: P. Jiang, Y. Uematsu, T. Yamagata | |
| Bias correction in factor-augmented regression models |
| Session CO176 | Room: K2.41 |
| Exteme risks analysis and real life applications | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Maud Thomas | Organizer: Maud Thomas |
| A0689: N. Madhar, J. Legrand, M. Thomas | |
| Assessing extreme risk using stochastic simulation of extremes | |
| A0794: N. Meyer | |
| Modeling moderate and extreme rainfall at high spatiotemporal resolution | |
| A1007: O. Lopez | |
| Parametric reinsurance for extreme claims | |
| A1081: A. Heranval, T. Opitz, D. Allard | |
| Analyzing the dynamics of extreme events with marked point processes |
| Session CO340 | Room: S0.03 |
| Probabilistic prediction of complex data | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Matteo Fontana | Organizer: Matteo Fontana |
| A0359: A. Gonzalez Sanz | |
| Nonparametric multiple-output center-outward quantile regression | |
| A0453: S. Lerch | |
| Generative machine learning methods for multivariate ensemble postprocessing | |
| A0925: J. Taylor, X. Meng | |
| Angular combining of forecasts of probability distributions: Applications and developments | |
| A0941: J. Cherian, I. Gibbs, E. Candes | |
| Large language model validity via enhanced conformal prediction methods |
| Session CO045 | Room: S0.11 |
| Spatial inference for fMRI analysis | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Armin Schwartzman | Organizer: Armin Schwartzman |
| A1647: A. Schwartzman, J. Ren, F. Telschow | |
| Quantifying the spatial uncertainty of excursion sets | |
| A1629: F. Telschow, A. Schwartzman, J. Ren | |
| Simultaneous confidence regions of excursion sets | |
| A1626: T. Maullin-Sapey | |
| Spatial confidence regions for combinations of excursion sets in image analysis | |
| A1630: S. Davenport | |
| Transforming heavy tailed data improves the power and validity of inference |
| Session CO229 | Room: S0.12 |
| Extremes and Levy processes | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Ana Ferreira | Organizer: Ana Ferreira |
| A0226: F. Brueck | |
| General graphical models for stable processes | |
| A0346: V. Fasen-Hartmann, B. Das | |
| On asymptotic independence in higher dimensions | |
| A0349: B. Das, X. Liu | |
| Assessing causality in the tails: Measurement and testing | |
| A1038: B. Buchmann | |
| Weak subordination of multivariate Levy processes |
| Session CO007 | Room: S0.13 |
| Recent topics in biostatistics | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Kathrin Moellenhoff | Organizer: Kathrin Moellenhoff |
| A0348: N. Hagemann, K. Moellenhoff | |
| Overcoming model uncertainty: How equivalence tests can benefit from model averaging | |
| A0557: C. Herrmann | |
| Accounting for delayed responses in group sequential clinical trial designs | |
| A0564: D. Ravi, A. Groll | |
| Enhancing time-to-event prediction with high-dimensional omics data using exclusive Lasso regularization | |
| A0929: C. Staerk, H. Klinkhammer, C. Maj, A. Mayr | |
| Recent challenges and biostatistical approaches in polygenic risk score modelling |
| Session CO103 | Room: S-1.01 |
| HiTEc: Recent progress in high-dimensional time series | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Marc Hallin | Organizer: Marc Hallin |
| A0300: Y. Goto, N. Bennala, M. Hallin | |
| Optimal tests for the absence of random individual effects in large n and small T dynamic panels | |
| A0366: P. Gersing, C. Rust, M. Deistler, M. Barigozzi | |
| Weak factors are everywhere | |
| A0439: L. Margaritella, J. Krampe | |
| Global bank network connectedness revisited: What is common, idiosyncratic and when | |
| A0516: A. Giovannelli, T. Proietti | |
| Spectral-based variable selection of high-dimensional data for prediction of the El Nino/Southern Oscillation cycle |
| Session CO058 | Room: S-1.04 |
| Recent developments in statistical modeling for stochastic processes | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Masayuki Uchida | Organizer: Masayuki Uchida |
| A0370: Y. Shimizu | |
| Statistical inference for generalized Gerber-Shiu functions in risk theory | |
| A0446: Y. Tonaki, Y. Kaino, M. Uchida | |
| Parametric estimation for a linear parabolic SPDE in two space dimensions under small diffusivity asymptotics | |
| A1054: M. Bibinger | |
| Statistical analysis of a stochastic boundary model for high-frequency data from a limit order book | |
| A0413: N. Yoshida | |
| Log-rank test with coarsened exact matching |
| Session CO097 | Room: S-1.06 |
| Contemporary directional statistics | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Andriette Bekker | Organizer: Andriette Bekker |
| A0376: T.L.J. Ng, A. Zammit Mangion, K.-K. Kwong, J. Liu | |
| Bayesian inference for sphere-on-sphere regression with optimal transport map | |
| A0545: J. Ameijeiras-Alonso | |
| A data-driven smoothing parameter for circular kernel density estimation | |
| A1214: S. Sarkar, Y. Zhang, Y. Chen, X. Zhu | |
| On regime changes in text data using hidden Markov model of contaminated von Mises-Fisher distribution | |
| A1513: J. Ferreira, A. Bekker, D. van Wyk de Ridder | |
| Insights into the construction of an alternative bivariate cardioid distribution |
| Session CO314 | Room: S-1.27 |
| Recent advances of statistical inference | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Yang Han | Organizer: Weichi Wu |
| A0852: L. Wang, Y. Han, W. Liu, F. Bretz | |
| Prediction and calibration for all future values: Simultaneous tolerance regions for multivariate regression | |
| A0896: X. Fan, W. Wu | |
| Random interval distillation for detection of change-points in Markov chain Bernoulli networks | |
| A0936: L. Bai, W. Wu | |
| Uniform variance reduced simultaneous inference of time-varying correlation networks | |
| A1304: Y. Cui | |
| Fully functional sieve covariance inference of locally stationary functional time series |
| Session CO381 | Room: S-2.25 |
| Recent advances in theory and methods of dependent data | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: HaoYun Huang | Organizer: Wei-Ying Wu |
| A0728: J. Yang | |
| Pseudo-spectra of multivariate inhomogeneous spatial point processes | |
| A0850: H. Huang | |
| Multi-resolution spatial methods on the sphere | |
| A1155: H.-D. Yang | |
| Modeling longitudinal area data with zero-modified via GEE | |
| A1209: W.-Y. Wu | |
| Tail estimation of the spectral density under fixed domain asymptotics |
| Session CO077 | Room: BH (S) 2.01 |
| Modelling risk and uncertainty | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Paulo Rodrigues | Organizer: Paulo Rodrigues |
| A1479: A. Souto de Moura, L. Emter, R. Setzer, N. Zorell | |
| Monetary policy and growth-at-risk: The role of institutional quality | |
| A1506: P. Raposo, P. Portugal, P. Rodrigues | |
| A new decomposition method using expectiles | |
| A1528: P. Rodrigues, J. Nicolau | |
| A simple but powerful tail index regression | |
| A1564: H. Reis | |
| Risk and heterogeneity in benefits from vocational versus general secondary education |
| Session CO281 | Room: BH (S) 2.02 |
| Econometrics of growth convergence and energy markets | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Joachim Schnurbus | Organizer: Joachim Schnurbus |
| A0668: M. Tomal | |
| A review of Phillips-Sul approach-based club convergence tests | |
| A1552: J. Schnurbus, H. Haupt, W. Semmler | |
| Convergence clubs in the European Union | |
| A1616: C. Amado, I. Garron Vedia, H. Veiga | |
| Measuring geopolitical risk on energy inflation: A panel quantile approach | |
| A1483: J. Ascorbebeitia, S. Orbe, E. Ferreira | |
| Sector risk in the European stock market: Navigating the energy transition era |
| Session CO079 | Room: BH (S) 2.03 |
| Models with large dimensional and functional variables | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Yoosoon Chang | Organizer: Yoosoon Chang |
| A1213: S. Kim, Y. Chang, Y. Choi, J. Park | |
| Surfing the cross-sectional density of characteristics for factor timing | |
| A1225: H. Mumtaz | |
| Risk and monetary policy in a data-rich model | |
| A1354: N. Sudo | |
| On the source of seasonality in price changes: The role of seasonality in menu costs | |
| A1623: M. Shintani, Y. Chang, J. Park | |
| Yield curve control policy in Japan: A functional error-correction model approach |
| Session CO071 | Room: BH (S) 2.05 |
| Markov switching processes and applications | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Maddalena Cavicchioli | Organizer: Maddalena Cavicchioli |
| A0390: T. Wozniak, A. Camehl | |
| Time-varying identification of monetary policy shocks | |
| A0338: J. Cheng | |
| Multivariate Markov switching BEKK models: Filtering, estimation and data analysis | |
| A1256: F. Demaria, M. Cavicchioli, U. Kocollari, F. Bertacchini | |
| Triple-win performance measurement for sustainable supply chains: A Markov-switching decision trees approach | |
| A0317: M. Cavicchioli, A. Ghezal, I. Zemmouri | |
| Bispectral analysis of Markov switching bilinear models |
| Session CO231 | Room: BH (SE) 1.02 |
| Prediction and clustering under Bayesian mixture models | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Raffaele Argiento | Organizer: Raffaele Argiento, Alessandra Guglielmi |
| A0302: A. Cremaschi, B. Franzolini | |
| Matrix-variate priors for flexible mixture modelling of grouped data | |
| A0304: B. Gruen, G. Malsiner-Walli, S. Fruehwirth-Schnatter | |
| Clustering categorical data using a Bayesian mixture of finite mixtures of latent class analysis models | |
| A0442: F. Camerlenghi, L. Ghilotti, T. Rigon | |
| Predictive inference for ecological problems | |
| A1178: E. Matechou | |
| Parametric, nonparametric and repulsive mixture models for ecological data |
| Session CO127 | Room: BH (SE) 1.05 |
| Time series modeling in finance and insurance | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Edit Rroji | Organizer: Edit Rroji |
| A0287: G. Apicella, E. Di Lorenzo, G. Piscopo, M. Sibillo | |
| Lee-Carter model: Biases and risk in the estimation of the gender mortality gap | |
| A0288: M. Azzone, R. Baviera, P. Manzoni | |
| The puzzle of carbon allowance spread | |
| A0399: A. Perchiazzo, L. Mercuri, E. Rroji | |
| Pricing options with a compound CARMA(p,q)-Hawkes model | |
| A1151: E. Allaj | |
| Identifying the number of latent factors of stochastic volatility models |
| Session CO016 | Room: BH (SE) 1.06 |
| HiTEc: Advances in finance and statistics | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Davide Lauria | Organizer: Davide Lauria, Sandra Paterlini |
| A0172: A. Fulci | |
| Sparsity-constrained estimators for graphical models | |
| A0880: S. Paterlini, M. Benuzzi, O. Sahin | |
| Evaluating the impact of methodological choices on ESG scores | |
| A1477: D. Lauria, R. Giacometti, G. Torri | |
| Distributionally robust optimal portfolios and ESG ambiguity | |
| A1370: P. Staehli, D. Maringer | |
| Start-to-low drawdown as a risk measure and its application to levered investor portfolio optimization |
| Session CO389 | Room: BH (SE) 2.01 |
| Perspective in exploring dependency | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: ShengLi Tzeng | Organizer: ShengLi Tzeng |
| A0801: J.-H. Shih, Y.-H. Chen | |
| Measuring multivariate regression association via spatial signs | |
| A0837: S. Tzeng | |
| A segmentation method for exploring multivariate spatiotemporal data | |
| A0838: C. Chang | |
| An algorithm for estimating threshold boundary regression models | |
| A0857: C.-S. Chen, C.-W. Shen, B.-R. Hsu | |
| Extreme value analysis using semiparametric spatial zero-inflated models |
| Session CO306 | Room: BH (SE) 2.05 |
| Macroeconomic consequences of climate change | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Francesco Simone Lucidi | Organizer: Francesco Simone Lucidi |
| A0576: D. Di Francesco, F. Lamperti, C. Brownlees | |
| Climate growth-at-risk | |
| A1233: I. Tahri, L. Mateane, W. Semmler | |
| Green assets, risk preferences and portfolio selection with sustainability | |
| A1544: M.M. Pisa | |
| Global food price, weather shocks, and inventory | |
| A1690: F.S. Lucidi | |
| Errors in temperature forecasts and energy prices |
| Session CO004 | Room: BH (SE) 2.09 |
| Macro-finance applications of quantile VAR models | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Simone Manganelli | Organizer: Simone Manganelli |
| A0214: Y. Schueler | |
| The global financial cycle and macroeconomic tail risks | |
| A0553: F. Lund-Thomsen, S. Chavleishvili, M. Kremer | |
| Quantifying financial stability trade-offs for monetary policy | |
| A0781: M. Schroder | |
| Estimating multivariate macroeconomic risk | |
| A1657: N. Maffei Faccioli | |
| The effects of monetary policy on macroeconomic risk |
| Session CO356 | Room: BH (SE) 2.12 |
| Topics in economic policy and international finance | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Christian Proano | Organizer: Christian Proano |
| A1453: M. Waechter | |
| How fitting is one-size-fits-all: Revisiting the dynamic effects of ECB's interest policy on Euro area countries | |
| A1466: C. Proano | |
| Output gap uncertainty, fiscal policy and risk premia under endogenous credibility | |
| A1540: L. Quero Virla, C. Proano | |
| Commodity price shocks, geopolitical risk, and macroeconomic activity | |
| A1556: L. Mateane, C. Proano | |
| Borrower-and lender-based macroprudential policies: How do they affect the transmission mechanism of fiscal policy |
| Session CO134 | Room: Safra Lec. Theatre |
| Advances in functional and object oriented data analysis | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Alessia Pini | Organizer: Alessia Pini |
| A0574: M. Ofner, S. Hoermann, D. Kraus, D. Liebl | |
| Testing missing completely at random for partially observed functional data | |
| A1226: A. Stamm, L. Bellanger, M. Bornet, K. Le Gall, N. Negab, M. Simonot | |
| Statistical analysis of trajectories of 3-dimensional object orientations | |
| A1421: A. Calissano, A. Okenov, A. Panfilov, T. Nezlobinsky | |
| Metric statistics for geometric graphs with application to cardiac fibrosis | |
| A0599: A. Casa, F. Ferraccioli, M. Stefanucci | |
| Adaptive functional regression for locally heterogeneous spectroscopic data |
| Session CO237 | Room: K2.31 (Nash Lec. Theatre) |
| High-dimensional statistics for genomics and biomedicine | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Mayetri Gupta | Organizer: Mayetri Gupta |
| A0566: V. Davies, N. Terzis, A. Elliott, R. Daly, J. Wandy | |
| Statistical and machine learning models from resolving metabolomics spectra | |
| A0993: H. Ruffieux, S. Jaoua, D. Temko | |
| A Bayesian functional factor model for high-dimensional molecular curves | |
| A1073: A. Khamseh, S. Beentjes, C. Ponting, O. Labayle, M. van der Laan, K. Tetley-Campbell, J. Slaughter | |
| Challenges in estimation of small genetic effects in large-scale population cohorts | |
| A1665: T. Kettlewell, Y. Cheng, T. Otto, V. Macaulay, M. Gupta | |
| ZINBGT: Exploratory data analysis of transcriptomic expression using mixture models |
| Session CO122 | Room: BH (S) 1.01 Lec. Theathre 1 |
| Modelling non-stationarity and structural change | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Liudas Giraitis | Organizer: Liudas Giraitis |
| A0814: L. Giraitis, K. Abadir, N. Bailey, W. Distaso | |
| Estimation of random cycles in persistent time series | |
| A0702: S. Richter, W.B. Wu, Z. Lou, J. Li | |
| Asymptotic theory for constant step size stochastic gradient descent | |
| A0900: Y. Li, L. Giraitis, G. Kapetanios | |
| Regression with lagged variables in heterogeneous environment | |
| A0629: J. Duffy, S. Mavroeidis | |
| Common trends and long-run identification in nonlinear structural VARs |
| Session CC442 | Room: BH (SE) 1.01 |
| Bayesian econometrics | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Rodney Strachan | Organizer: CFE-CMStatistics |
| A0289: S. Smith, H.J. Ahn | |
| Inflation dynamics during the COVID-19 era: A high-frequency approach | |
| A0441: S. Vahap | |
| Bayesian dynamic graphical models for large vector autoregressions with time-varying parameters and volatility | |
| A1401: M.A. Rahman, S. Karnawat, I. Jeliazkov, A. Vossmeyer | |
| Flexible Bayesian quantile analysis of residential rental rates | |
| A1333: R. Strachan, E. Eisenstat | |
| Singular vector autoregressions |
| Session CC496 | Room: BH (SE) 2.10 |
| Financial econometrics I | Sunday 15.12.2024 08:45 - 10:25 |
| Chair: Toshiaki Watanabe | Organizer: CFE-CMStatistics |
| A1653: L.V. Ballestra, E. DInnocenzo, C. Tezza | |
| A GARCH model with two volatility components and two stochastic factors | |
| A1250: T. Dimpfl, D. Baur, J. Pena | |
| A safe haven index | |
| A1440: A.A. Musa, Y. Feng | |
| A semiparametric semi-strong FARIMA with heteroskedastic errors applied to energy market | |
| A0368: S.H. Choi, D. Kim | |
| Matrix-based prediction approach for intraday instantaneous volatility vector |
| Parallel session G: CFECMStatistics2024 | Sunday 15.12.2024 | 10:55 - 12:10 |
| Session CO367 | Room: K0.18 |
| Novel statistical tools for biomedical research | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Claudia Solis-Lemus | Organizer: Claudia Solis-Lemus |
| A1418: C. Solis-Lemus | |
| Bayesian chain graph model for microbiome data | |
| A1507: S. Chen, D. Alvares, C. Jackson, S. Richardson, J. Barrett | |
| Bayesian joint models for longitudinal multimorbidity analysis | |
| A1101: S. Tiberi, J. Bollon | |
| IsoBayes: A Bayesian approach for single-isoform proteomics inference |
| Session CO253 | Room: K0.19 |
| Advance in modern data analysis | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Daren Wang | Organizer: Daren Wang |
| A0769: L. Chu | |
| A Graph-based Approach to Estimating the Number of Clusters | |
| A1295: J. Park | |
| Parameter inference for partially observed, implicitly defined simulation models | |
| A1624: Y. Peng, Y. chen, M. Stoudenmire, Y. Khoo | |
| Generative modeling via hierarchical tensor sketching |
| Session CO251 | Room: K0.20 |
| Recent advances in sufficient dimension reduction | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Wei Luo | Organizer: Wei Luo |
| A0270: W. Luo, Y. Jin | |
| Fast fitting of Gaussian mixture model via dimension reduction | |
| A0532: J. Zeng | |
| Dimension reduction for extreme regression via contour projection | |
| A0605: A. Artemiou | |
| Dimension reduction for multivariate time series | |
| A1726: S. Ding | |
| Nonconvex-regularized integrative sufficient dimension reduction for multi-source data |
| Session CO209 | Room: K0.50 |
| Advances in optimal experimental design | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Sergio Pozuelo Campos | Organizer: Sergio Pozuelo Campos |
| A0559: R. Negrete Gallego, I. Garcia Camacha Gutierrez, S. Pozuelo Campos | |
| Optimizing active power in electrical systems through optimal experimental design with piezoelectric paints | |
| A0681: A. Munoz, V. Casero-Alonso, M. Amo-Salas | |
| Optimal designs for the Baranyi model with two controllable variables | |
| A0919: S. Leorato, C. Tommasi, C. de la Calle-Arroyo, L. Rodriguez-Aragon | |
| Augmented designs to choose between constant absolute and relative errors in regression models |
| Session CO002 | Room: K2.40 |
| Recent advances in statistical methods for practical applications | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Rui Pan | Organizer: Rui Pan |
| Session CO346 | Room: S0.03 |
| Mathematics of deep-learning for solving differential equations | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Anirbit Mukherjee | Organizer: Anirbit Mukherjee |
| A0277: E. Bach, T. Colonius, I. Scherl, A. Stuart | |
| Filtering dynamical systems using observations of statistics | |
| A0881: D. Kumar, A. Mukherjee | |
| Measuring the risk of solving fluid dynamics by neural nets | |
| A1052: J. Zech, N. Reinhardt, S. Wang | |
| Statistical learning theory for neural operators |
| Session CO300 | Room: S0.11 |
| Statistical methods for high-dimensional neuroimaging and time series data | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Ali Shojaie | Organizer: Ali Shojaie |
| A0724: S. Chen, Z. Jiang, Y. Liu | |
| Instrumental variable analysis with multivariate point process treatments | |
| A0839: M. Hellstern, A. Shojaie, B. Kim | |
| Spectral differential network analysis for high-dimensional time series | |
| A1037: A. Safikhani | |
| Transfer learning for high-dimensional reduced rank time series models |
| Session CO312 | Room: S0.12 |
| Analysis of extreme values: Theory and applications | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Boris Beranger | Organizer: Boris Beranger |
| A1097: P. Zhong, S. Sisson, B. Beranger | |
| Flexible max-stable processes for fast and efficient inference | |
| A1106: L. Zhang, C. Wikle | |
| Modeling high and low extremes with a novel dynamic spatiotemporal model | |
| A1148: M. Thannheimer, M. Oesting | |
| Bayesian inference for functional extreme events defined via partially unobserved processes |
| Session CO105 | Room: S0.13 |
| Uncertainty quantification | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: David Woods | Organizer: David Woods |
| A0372: V. Volodina | |
| Comparative analysis of model discrepancy treatment: Calibration versus scientific machine learning | |
| A0625: D. Ming | |
| Linked deep Gaussian process emulation of model networks | |
| A0928: S. Jackson, D. Woods | |
| Bayes linear analysis with uncertain covariates |
| Session CO085 | Room: S-1.01 |
| HiTEc: Topics in financial econometrics and regime switching models | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Leopold Soegner | Organizer: Joern Sass, Leopold Soegner |
| A0864: M. Abdollahi, L. Soegner | |
| Monitoring structural breaks in vector autoregressive models | |
| A1044: F. Schirra, S. Schwaar, J. Sass | |
| A change point test for a gradual change in the Poisson INARCH(1)-process | |
| A1292: Y. Le Pen, A. Thomas, Z. Moussa | |
| Regime switching for dynamic equicorrelation |
| Session CO339 | Room: S-1.04 |
| Applications of point process and related models for counts (virtual) | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Satish Iyengar | Organizer: Satish Iyengar |
| Session CO401 | Room: S-1.27 |
| Machine learning and global health | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Alexandra Blenkinsop | Organizer: Alexandra Blenkinsop |
| A0249: X. Xi, H. Ruffieux | |
| Detecting and leveraging node-level information in network inference | |
| A0307: O. Ratmann | |
| Fast heavy-tail count models for automated probabilistic computing and pandemic preparedness | |
| A0671: M. Zhang | |
| Sequential decision-making in public health |
| Session CO336 | Room: BH (S) 2.01 |
| Advances in financial econometrics and risk analytics | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Mike So | Organizer: Mike So |
| A0590: T. Watanabe, J. Nakajima | |
| Bayesian analysis of long memory and roughness in financial volatility | |
| A0666: C. Sin | |
| On "sandwich" variance estimation: Bayesian versus frequentist | |
| A0382: R. Gerlach, R. Peiris, C. Wang, M.-N. Tran | |
| Semi-parametric financial risk forecasting incorporating multiple realized measures |
| Session CO322 | Room: BH (S) 2.02 |
| Statistics for complex-valued time series and integer-valued time series | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Yan Liu | Organizer: Yan Liu |
| A0552: H. Ogata | |
| Expression of circular time series models with complex-valued stochastic processes | |
| A0927: T. Shiohama, H. Ogata | |
| Recent developments in complex-valued and circular time series modeling | |
| A1264: M. Faymonville, C. Jentsch, E. Paparoditis | |
| Predictive inference for discrete-valued time series |
| Session CO158 | Room: BH (S) 2.03 |
| Financial econometrics: Bubbles and forecasting | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Robinson Kruse-Becher | Organizer: Robinson Kruse-Becher |
| A0222: E. Pavlidis | |
| Bubbles and crashes: A tale of quantiles | |
| A0431: D. Thomakos | |
| Fundamentals of financial forecasting: Simplicity vs. complexity | |
| A0309: C. Wegener, T. Basse, M. Lamla, S. Maiani | |
| The everything bubble: What is behind the surge in US equity prices |
| Session CO275 | Room: BH (S) 2.05 |
| J-ISBA session on recent advances in Bayesian statistics | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Andrea Cremaschi | Organizer: Matteo Giordano |
| A0822: L. Travis | |
| Bayesian methods for regression with confounding variables | |
| A0883: L. Alamichel, J. Arbel, G. Kon Kam King, I. Pruenster | |
| Bayesian nonparametric mixture models and clustering for ecological risks | |
| A0924: Y. Zhu, B. Szabo | |
| Vecchia Gaussian processes: Probabilistic properties and Bayesian nonparametrics |
| Session CO202 | Room: BH (SE) 1.01 |
| Bayesian econometrics in economics and finance | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Aristeidis Raftapostolos | Organizer: Jamie Cross |
| A1211: T. Szendrei, D. Kohns | |
| Joint quantile shrinkage: Toward non-crossing Bayesian quantile models | |
| A1251: M. Zaharieva, P. Wu | |
| Variable ordering in a Cholesky- multivariate stochastic volatility model | |
| A1695: A. Raftapostolos, I. Chronopoulos, G. Kapetanios | |
| Nuclear norm penalised non-linear modelling for asset pricing |
| Session CO047 | Room: BH (SE) 1.02 |
| Bayesian computation | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Michael Daniels | Organizer: Michael Daniels |
| A1267: N. Heard | |
| Uncertainty quantification in latent position graph models | |
| A1366: M. Xu | |
| Efficient Bayesian inference on sparse and low-rank covariance matrices via projection | |
| A1489: S. Bhadra, M.J. Daniels | |
| Variational Bayes and truncation approximations for enriched Dirichlet process mixtures |
| Session CO185 | Room: BH (SE) 1.05 |
| Econometrics and forecasting for economic and financial markets | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Rustam Ibragimov | Organizer: Rustam Ibragimov |
| A1093: A. Hasanov | |
| Abrupt variance shifts and volatility forecasting in the renewable energy markets: A comprehensive analysis | |
| A1662: R. Ibragimov, J. Hau-Ruess, A. Min | |
| Robust inference in predictive regressions for stock returns | |
| A1056: N. Abdullaev, R. Ibragimov | |
| Stylized facts of cryptocurrency markets: Robust definitions and inference approaches |
| Session CO106 | Room: BH (SE) 1.06 |
| Modelling investment decision making and asset pricing | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Michail Karoglou | Organizer: Michail Karoglou |
| A1057: D. Stafylas, E. Platanakis, C. Sutcliffe, W. Zhang | |
| Hedge fund performance, classification with machine learning, and managerial implications | |
| A1058: E. Platanakis, X. Huang, D. Newton, X. Ye | |
| When Black-Litterman meets decision-fusion for asset allocation | |
| A1078: P. Li, J. Guo, G. Korniotis, A. Kumar | |
| Empirical decisions and replicating anomalies: The benefit of the aggregate average |
| Session CO133 | Room: BH (SE) 2.01 |
| y-SIS - Advances in statistical learning and clustering | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Carlo Zaccardi | Organizer: Cinzia Di Nuzzo, Giorgia Zaccaria |
| A0404: A. Bitetto, M. Modina, S. Filomeni | |
| Understanding corporate default: The role of accounting and market information with a cluster-based matching procedure | |
| A0876: E. Seri, I. Bombelli, M. Vichi, S. Iezzi | |
| Spherical double k-means | |
| A0982: L. Scaffidi Domianello, S. Ingrassia | |
| Contrastive learning: A statistical approach |
| Session CO284 | Room: BH (SE) 2.05 |
| Modelling and analysing the transition to green energy | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Stefan Wrzaczek | Organizer: Stefan Wrzaczek |
| A0620: S. Wrzaczek, D. Grass, M. Kuhn, A. Prskawetz, O. Patange | |
| Transition out of coal: A model based framework | |
| A1066: F. Cabo, M. Molpeceres-Abella | |
| On green reputation | |
| A1165: L. Grossi, F. Beltrami, M. Liebensteiner | |
| Marginal emission factors in electricity markets: The role of renewable energy sources and electricity trade |
| Session CO308 | Room: BH (SE) 2.09 |
| Advances in macroeconometrics | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Danilo Leiva-Leon | Organizer: Danilo Leiva-Leon |
| A0455: A. Renzetti | |
| Theory coherent shrinkage of time-varying parameters in VARs | |
| A1034: G. Martorana, E. Castelnuovo, A. Margaris | |
| Dancing with the R-star: Information Shocks in the "New Normal" | |
| A1045: D. Leiva-Leon, R. Sekkel, L. Uzeda | |
| Do Monetary Policy Shocks Affect the Neutral Rate of Interest? |
| Session CO182 | Room: BH (SE) 2.10 |
| Analysis of complex multivariate data | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Thomas Verdebout | Organizer: Thomas Verdebout |
| A1457: J. Trufin, M. Denuit | |
| Autocalibrated predictors in nonlife insurance pricing | |
| A1485: T. Verdebout | |
| Inference for nearly directional data | |
| A1492: M. Boucher, T. Verdebout, Y. Goto, C. Francq | |
| On runs tests for directional data and their local and asymptotic optimality properties |
| Session CO204 | Room: K2.31 (Nash Lec. Theatre) |
| Inference for federated learning and synthetic control | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Jia Gu | Organizer: Yumou Qiu |
| A1444: J. Gu, S. Chen | |
| Statistical inference for decentralized federated learning | |
| A1486: S. Wang, S. Chen, X. Zheng | |
| Dynamic synthetic control method for semiparametric time-varying models | |
| A1532: K. Morikawa | |
| Model-free data integration for estimation of average treatment effect in randomized clinical trial |
| Session CO014 | Room: BH (S) 1.01 Lec. Theathre 1 |
| Time series econometrics | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Antonio Montanes | Organizer: Antonio Montanes |
| A1458: M. Camarero | |
| Drivers of portfolio equity and bond investment in the European Union: The interplay of tax havens and gravity factors | |
| A1459: A. Montanes | |
| Estimation of the autoregressive parameter under the presence of outliers | |
| A1614: J.L. Carrion-i-Silvestre | |
| Residual-based tests for cointegration involving bounded stochastic processes |
| Session CC502 | Room: K0.16 |
| Computational and methodological statistics | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Jan Gertheiss | Organizer: CFE-CMStatistics |
| A1718: N. Weeraratne, L. Hunt, J. Kurz | |
| Improving finite sample estimates of principal components for high-dimensional data | |
| A1719: J. Shin, S.J. Shin | |
| Computationally efficient sparse sufficient dimension reduction via least squares svm and its extensions | |
| A1704: I. Medovikov | |
| Dependence maps: A graphical tool for visualizing multivariate dependence |
| Session CC413 | Room: K2.41 |
| Survival analysis | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Kathrin Moellenhoff | Organizer: CFE-CMStatistics |
| A1625: B. Monroy-Castillo, M.A. Jacome, R. Cao | |
| Testing the effect of multiple covariates on the cure rate using martingale difference correlation | |
| A1429: A. Schenk, M. Schmid | |
| Modeling the restricted mean survival time as a function of time horizons with pseudo-value regression trees | |
| A0361: L. Machado, M. Azevedo, G. Soutinho | |
| Estimation in a three-state model with interval-censored data |
| Session CC497 | Room: S-1.06 |
| Bayesian causal inference | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Michael Schweinberger | Organizer: CFE-CMStatistics |
| A0782: C. Kim, C. Zigler | |
| Bayesian ensemble learning for principal causal effects | |
| A1484: S. Horii, Y. Chikahara | |
| Bayesian estimation of causal effects from multiple datasets using structural causal models | |
| A0235: A. Mercatanti, S. Guha, T. Makinen | |
| A Bayesian model for estimating spillover effects in unconventional monetary policy |
| Session CC470 | Room: S-2.25 |
| Methodological statistics | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Maria Brigida Ferraro | Organizer: CFE-CMStatistics |
| A1562: G. Uwimpuhwe, R. Drikvandi | |
| On testing random effects in linear and non-linear mixed effects models | |
| A1347: S. Ahn | |
| Novel constraints in empirical likelihood for ranked set sampling | |
| A1417: M. Alvo, X. Duan | |
| Model fitting using partially ranked data |
| Session CC493 | Room: BH (SE) 2.12 |
| Risk management and portfolio optimization | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Pavlo Mozharovskyi | Organizer: CFE-CMStatistics |
| A1339: S. Park | |
| Optimal consumption and investment decisions with disastrous income risk | |
| A1678: F. Ielpo, J. Royer, S. Muhammetgulyyeva | |
| Regime parity | |
| A1500: M. Maggi, P. Uberti | |
| A bootstrap equality test to compare portfolio value at risk and expected shortfall |
| Session CC431 | Room: Safra Lec. Theatre |
| Functional data analysis | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Alessia Pini | Organizer: CFE-CMStatistics |
| A1300: G. Van Bever, J.M. Jeon | |
| Additive regression for Riemannian functional responses | |
| A1523: I.-A. Moindjie, M.-H. Descary, C. Beaulac | |
| A fully functional approach for statistical shape analysis | |
| A1573: X. Xu | |
| Generalized functional probabilistic principal component analysis for longitudinal microbiome data analysis |
| Session CP001 | Room: Auditorium |
| Poster Session | Sunday 15.12.2024 10:55 - 12:10 |
| Chair: Cristian Gatu | Organizer: CFE-CMStatistics |
| Parallel session H: CFECMStatistics2024 | Sunday 15.12.2024 | 13:40 - 15:20 |
| Session CI054 (Special Invited Session) | Room: Auditorium |
| Advances in modelling financial and economic uncertainty | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Svetlana Makarova | Organizer: Svetlana Makarova |
| A0159: K. Shields, K. Lee, G. Turnip | |
| Shock persistence, uncertainty and news-driven business cycles | |
| A0160: G. Piccillo, S. Mohades, T. Treibich | |
| Unpacking economic uncertainty: Measuring the firm, sector and aggregate components | |
| A0161: A. Azqueta-Gavaldon, J.J. Perez, M. Diakonova, C. Ghirelli, G. Tobias | |
| Unveiling the sources of economic uncertainty |
| Session CO135 | Room: K0.16 |
| Recent advances in networks and high dimensional data | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Vince Lyzinski | Organizer: Vince Lyzinski |
| A0795: M. Pensky | |
| Signed diverse multiplex networks: Clustering and inference | |
| A0509: B. Leinwand, V. Pipiras | |
| Augmented degree correction for bipartite networks with applications to recommender systems | |
| A0806: R. Zheng, M. Tang | |
| Multi-source matrix data integration via embedding alignment | |
| A0832: Z. Li, K. Levin, Z. Zhao, V. Lyzinski | |
| Matching and mixing: Matchability of graphs under Markovian error |
| Session CO096 | Room: K0.18 |
| Innovative statistical approaches for climate change studies | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Andriette Bekker | Organizer: Andriette Bekker |
| A0695: M. Mingione | |
| An axial regression model to detect vegetation growth patterns | |
| A0953: P. Nagar, A. Kheyri, A. Bekker | |
| Exploring pollutant patterns through graphical elastic net | |
| A1099: J. Van Niekerk, H. Rue | |
| INLA: A computational tool for climate modeling | |
| A1110: A. Borges, C. Cordeiro, M.R. Ramos, M. Carvalho | |
| Detecting anomalies in emission trends: A statistical approach for environmental policy in Portugal |
| Session CO091 | Room: K0.20 |
| Recent developments on data depth and functional data analysis | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Sara Lopez Pintado | Organizer: Sara Lopez Pintado |
| A0810: H. Yeon, X. Dai, S. Lopez Pintado | |
| Regularized halfspace depth: Practical insights and applications | |
| A0833: P. Mozharovskyi, J. Ivanovs | |
| Distributionally robust halfspace depth | |
| A1015: A. Arribas-Gil, S. Lopez Pintado | |
| Robust depth-based registration for multivariate functional data | |
| A0972: S. Lopez Pintado, S. Nagy, T. Ogden, M. Luo | |
| The quantile integrated depth with applications to noisy functional data |
| Session CO157 | Room: K0.50 |
| Recent advances in design and analysis of experiments | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Chenlu Shi | Organizer: Chenlu Shi |
| A0387: L. Pronzato | |
| Relaxed greedy packing for nested space-filling designs | |
| A0458: L. Wang | |
| Active sampling for high-dimensional ridge estimator with application in genome-wide association studies | |
| A0754: T. Dasgupta, S. Ghosh, K. Khamaru | |
| A sequential approach to obtain optimal designs for non-linear models harnessing closed-form solutions | |
| A0770: F.K.H. Phoa, Y.-H. Liao, D. Woods | |
| Summary of effect aliasing structure for design selection and factor-column assignment for supersaturated designs |
| Session CO331 | Room: K2.41 |
| Competing risks and dependent survival models with parametric elements | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Dennis Dobler | Organizer: Dennis Dobler |
| A0407: T. Emura | |
| Estimation for the Mann-Whitney effect under parametric survival copula models | |
| A0528: M. Overgaard | |
| A comparison of Kaplan-Meier-based inverse probability of censoring weighted regression methods | |
| A0613: K. Moellenhoff, N. Binder, H. Dette | |
| Testing similarity of parametric competing risks models for identifying potentially similar pathways in healthcare | |
| A1288: M. Dietrich, D. Dobler, M. de Gunst | |
| Inference via wild bootstrap and multiple imputation under fine-gray models with incomplete data |
| Session CO022 | Room: S0.11 |
| Statistics in neuroscience I | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Russell Shinohara | Organizer: Russell Shinohara |
| A0332: S. Weinstein | |
| Mapping individual differences in inter-modal coupling | |
| A0583: T. Johnson | |
| A time-varying AR, bivariate DLM of functional near-infrared spectroscopy data | |
| A0677: S. Vandekar | |
| Multivariate inference for effect size images | |
| A1150: J. Wrobel | |
| A case study of pupil dynamics after cannabis consumption using crossed multilevel function-on-scalar regression |
| Session CO188 | Room: S0.12 |
| Transformative approaches in machine learning | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Chuan Hong | Organizer: Chuan Hong |
| A1696: S. Berchuck, S. Mukherjee, A. Agazzi | |
| Scalable Bayesian inference for the generalized linear mixed model | |
| A1699: L. Tang | |
| Dynamic time-to-event prediction with ML/DL: Addressing competing risks in clinical outcomes | |
| A1700: N. Liu | |
| Interpretable machine learning-based scoring system for clinical decision making | |
| A1698: M. Liu | |
| Multi-source stable variable importance measure via adversarial machine learning |
| Session CO264 | Room: S0.13 |
| New advances in biostatistics | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Liqun Diao | Organizer: Liqun Diao |
| Session CO063 | Room: S-1.01 |
| HiTEc: Topics on high dimensional statistics | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Andreas Artemiou | Organizer: Andreas Artemiou |
| A0203: K. Ntotsis, A. Artemiou, A. Karagrigoriou | |
| Kernel association rotation analysis: A kernel-based projection of continuous data | |
| A0232: Y. Jin | |
| Inverse regression with column selection: A unified generalization of inverse regression via adaptive column selection | |
| A0501: E. Christou, E. Solea, S. Wang, J. Song | |
| Sufficient dimension reduction for conditional quantiles for functional data | |
| A0887: S. Roy, M. Nunes, X. Tian, A. Gibberd | |
| Multi-response linear regression estimation based on low-rank pre-smoothing |
| Session CO149 | Room: S-1.04 |
| Computational asymptotic statistics for stochastic processes | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Nakahiro Yoshida | Organizer: Nakahiro Yoshida |
| A0422: I. Muni Toke, T. Fabre | |
| High-frequency market manipulation detection with a Markov-modulated Hawkes process | |
| A1247: M. Rosenbaum | |
| Rough volatility estimation in a fully general framework | |
| A0999: A. Gloter, N. Yoshida | |
| Drift estimation for rough diffusion models under a small noise asymptotic assumption | |
| A0577: E. Guidotti, N. Yoshida | |
| Analytical approximations for diffusion processes with asymptotic expansion |
| Session CO183 | Room: S-1.06 |
| New challenges in modern statistics and data analysis (virtual) | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Yichuan Zhao | Organizer: Yichuan Zhao |
| Session CO221 | Room: S-1.27 |
| All models are wrong but many are useful | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Lucas Mentch | Organizer: Lucas Mentch |
| A1622: L. Mentch | |
| Forward stability and model path selection | |
| A0179: L. Semenova | |
| On the existence of simpler-yet-accurate models | |
| A0467: D. Ferrari | |
| Confidence sets for high-dimensional variable selection | |
| A0181: R. Cecil, L. Mentch | |
| Model class selection |
| Session CO368 | Room: S-2.23 |
| Industrial statistics (virtual) | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Wei-Heng Huang | Organizer: Wei-Heng Huang |
| Session CO072 | Room: S-2.25 |
| Sports analytics | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Christophe Ley | Organizer: Andreas Groll, Christophe Ley |
| A0779: I.-M. Berendes, A. Gerharz, A. Groll, M. Kolodziej | |
| Injury prediction in soccer with conventional statistical approaches and machine learning models | |
| A0860: L. Grassetti, V. Mameli, M. Lambardi di San Miniato | |
| A bivariate extension of the regularized adjusted plus-minus model | |
| A0962: K. Szczerba, L. Malisoux, C. Ley | |
| Comparison of prediction models for survival analysis of running-related injuries | |
| A1059: L. Martin | |
| From data to dominance: The new era of sports analytics |
| Session CO184 | Room: BH (S) 2.01 |
| Advances in time series and panel data econometrics | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Martin Wagner | Organizer: Martin Wagner |
| A1241: S. Veldhuis, M. Wagner | |
| Long-run money demand reconsidered | |
| A1183: L. Soegner, M. Wagner | |
| Online breakpoint - detection in cointegrating relationships | |
| A1167: M. Wagner, F. Knorre | |
| FM-OLS estimation and inference for SUCPRs with common integrated regressors | |
| A0234: J. Steenbergen, L. Catania | |
| Noise cancelling observation-driven models |
| Session CO390 | Room: BH (S) 2.02 |
| New directions in detecting changes and clusters | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Ansgar Steland | Organizer: Ansgar Steland |
| A1235: K. Egashira, K. Yata, M. Aoshima | |
| Asymptotic properties of k-means and its bias correction under high dimensional settings | |
| A1368: Y. Wu | |
| Optimal kernel smoothing method for detecting fixed and random mean change in multivariate data | |
| A1572: A. Steland | |
| General adapted-threshold monitoring in discrete environments and rules for imbalanced classes | |
| A1356: R. Basu | |
| Statistical inferences from biomechanical fatigue data of running athletes |
| Session CO326 | Room: BH (S) 2.03 |
| New developments in financial time series | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Richard Gerlach | Organizer: Cathy W-S Chen |
| A0412: L.-H. Sun | |
| Interval-based time series analysis: Detecting structural shifts and estimating change-points | |
| A0415: E.M.-H. Lin | |
| Nonlinear market dynamics: Range-based hysteretic volatility modeling | |
| A0435: S.-F. Huang | |
| Portfolio optimization based on dynamic networks and vine copulas | |
| A0438: M. Asai, B. Poignard | |
| Factor multivariate stochastic volatility models of high dimension |
| Session CO191 | Room: BH (S) 2.05 |
| Panel models | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Ralf Wilke | Organizer: Ralf Wilke |
| A0793: J.M. Rodriguez-Poo, A. Soberon, A. Musolesi | |
| A semiparametric panel data model with common factors and spatial dependence | |
| A1115: E. Aristodemou | |
| Binary response dynamic panel data models with switching state dependence | |
| A1163: M. Avila Marquez | |
| On the use of random forests to estimate triangular two-level panel data models with individual fixed effects | |
| A1164: M. Mugnier | |
| Fixed effects quantile regression via deconvolutional differencing in short panels |
| Session CO398 | Room: BH (SE) 1.01 |
| y-SIS: Recent advances in Bayesian methods | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Luca Aiello | Organizer: Beatrice Franzolini, Luca Aiello |
| A0319: L. Danese, A. Ongaro, R. Corradin, W.R. KhudaBukhsh | |
| Model based clustering of time-dependent observations with common historical shocks | |
| A0579: M. Grushanina, S. Fruehwirth-Schnatter | |
| Dynamic mixture of finite mixtures of factor analyzers | |
| A0776: M. Gianella, M. Beraha, A. Guglielmi | |
| Bayesian nonparametric boundary detection for income areal data | |
| A0851: D. Zorzetto | |
| Multivariate treatment effect estimation through Bayesian factor regression model |
| A0819: S. Filippi, M. Komodromos, K. Ray, M. Evangelou | |
| Group sparse variational Bayes approach for high-dimensional data | |
| A0318: V. Inacio | |
| Density regression via Dirichlet process mixtures of normal structured additive regression models | |
| A0331: R. Warr | |
| Bayesian mixture models for histograms: With applications to large datasets | |
| A1375: A. Beskos, M. De Iorio, W. van den Boom, A. Jasra, A. Cremaschi | |
| Graph of graphs: From nodes to supernodes in graphical models |
| Session CO387 | Room: BH (SE) 1.05 |
| Nowcasting and forecasting macroeconomic and financial risk | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Domenico Giannone | Organizer: Domenico Giannone |
| A0237: M. Luciani, M. Barigozzi, C. Lissona | |
| Measuring the Euro area output gap | |
| A0718: F. Loria | |
| Understanding growth-at-risk: A Markov-switching approach | |
| A0956: D. Giannone | |
| Forecasting macroeconomic risk with many predictors | |
| A1201: Y. Chang, S. Kim, J. Park, Y. Choi | |
| Market returns dormant in options panels |
| Session CO049 | Room: BH (SE) 1.06 |
| Climate and sustainable finance | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Michele Costola | Organizer: Monica Billio |
| A1533: C. Weng | |
| ESG-constrained portfolio choice with estimation risk | |
| A1593: F. Parla, A. Cipollini, F. Parla | |
| Hydrogeological risk and accessing credit for Italian SME | |
| A0444: C. Latino, Y. Wang, L. Pelizzon, M. Riedel | |
| Mutual funds' appetite for sustainability in European auto ABS | |
| A1179: M. Costola | |
| The incremental information content of ESG score in financial decision making |
| Session CO213 | Room: BH (SE) 2.01 |
| Multivariate dependence structures | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Zinoviy Landsman | Organizer: Zinoviy Landsman |
| A0276: T. Shushi | |
| Integrating ESG performance in traditional risk measures of mutually dependent risks | |
| A0283: Z. Landsman, U. Makov | |
| A minimum variance approach to linear regression with application to actuarial and financial problems | |
| A0436: N. Loperfido, C. Franceschini, N. Loperfido, M. Bruto | |
| From colors to numbers: Statistical methods for color theories | |
| A0452: M. Kelner | |
| Enhancing dependence modeling with compound Archimedean and vine copulas for electricity peak demand estimation |
| Session CO330 | Room: BH (SE) 2.05 |
| Supervised, deep, and reinforcement learning in economic and finance | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Tato Khundadze | Organizer: Tato Khundadze |
| A0486: T. Atashbar | |
| Multi-agent deep reinforcement learning and LLM-augmented frameworks for economic policy simulation | |
| A0497: G. Dufrenot, U. Aiounou | |
| A nonlinear Gegenbauer process to model the unemployment rate in the G7 countries | |
| A0662: G. Gopalakrishna | |
| Deep-MacroFin: Informed equilibrium neural network for continuous time economic models | |
| A0886: T. Khundadze, W. Semmler | |
| Modeling cooperative fiscal policy in the Euro area using reinforcement learning and NMPC |
| Session CO219 | Room: BH (SE) 2.10 |
| CWS session: Statistics and data science from women around the globe | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Cynthia Bland | Organizer: Vanda Lourenco |
| A0323: E. Moreira, M. Neves | |
| Modelling drought classes time series for groundwater drought assessment and prediction in Algarve region | |
| A0471: S. Jana | |
| GMANOVA modelling for volatile data | |
| A0792: V.A. Gonzalez-Lopez | |
| A metric based on the efficient determination criterion | |
| A0445: S. Lee | |
| Adapting student performance evaluation in the AI era |
| Session CO024 | Room: BH (SE) 2.12 |
| Recent advances in spatial and spatio-temporal data modeling | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Rajarshi Guhaniyogi | Organizer: Rajarshi Guhaniyogi |
| A1161: D. Li | |
| STimage-1K4M: A large scale dataset for spatial transcriptomics | |
| A1169: W. Kleiber | |
| Nonstationary elastic space-time (NEST) Kriging and solar irradiance studies | |
| A1176: L. Zhang, W. Tang, S. Banerjee | |
| Bayesian geostatistics using predictive stacking | |
| A1319: S. Saha, J. Bradley | |
| Markov random fields with proximity constraints for spatial data |
| Session CO348 | Room: Safra Lec. Theatre |
| Functional data analysis and stochastic processes | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Caterina May | Organizer: Caterina May |
| A0513: E. Bongiorno, K.L.L. Chan, A. Goia, P. Vieu | |
| A nonparametric method to detect the number of components for dimensionality reduction techniques | |
| A0517: A.M. Gambaro | |
| Exponential expansions for approximation of probability distributions | |
| A0866: S. Vantini, T. Bortolotti, E. Prioglio, B.M. Colosimo | |
| Anomaly detection in profile monitoring using functional conformal prediction | |
| A1141: A. Pini, M. Morvan, J.M. Giacofci, V. Monber | |
| Block testing covariance and precision matrices for functional data analysis |
| Session CO355 | Room: K2.31 (Nash Lec. Theatre) |
| Statistical learning in causal inference | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Debarghya Mukherjee | Organizer: Debarghya Mukherjee |
| A1042: S. Maity | |
| Learning the distribution map in reverse causal performative prediction | |
| A1083: C. Fogarty, M. Lin | |
| Controlling the false discovery proportion in observational studies with hidden bias | |
| A1098: A. Ghassami, I. Shpitser, E. Tchetgen Tchetgen | |
| Combining experimental and observational data for identification and estimation of long-term causal effects | |
| A1316: N. Deb, B. Karmakar, A. Ghosh, B. Sen | |
| Efficiency and robustness of Rosenbaum's rank-based estimator in randomized experiments |
| Session CO298 | Room: BH (S) 1.01 Lec. Theathre 1 |
| Forecasting and panel analysis | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Peter Pedroni | Organizer: Fakhri Hasanov, Peter Pedroni |
| A0162: D. Hendry, J.L. Castle, J. Doornik | |
| Forecasting after the start of a trend break | |
| A1697: U. Bahl | |
| Firm-level markups and monetary policy transmission: A panel time-series based analysis | |
| A1663: P. Pedroni, F. Hasanov | |
| Automatic stabilizers from fiscal policy and the role of public and private consumption | |
| A1660: F. Hasanov, P. Pedroni | |
| Innovation in solar energy technologies and the implications for environmental and economic sustainability |
| Session CC510 | Room: K0.19 |
| Statistical methods and applications II | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Kalliopi Mylona | Organizer: CFE-CMStatistics |
| A1733: L. Empting | |
| The pvars R-Package: VAR modeling for heterogeneous panels | |
| A1735: A. Sartore, D. Catelan, T. Fletcher, C. Canova, M. Berti, G. Stoppa, A. Biggeri | |
| Spatial causal analysis: Case study of testicular cancer and PFAS exposure in Veneto, Italy | |
| A1734: A. Kagan, J. Zhu, L. Levina | |
| Latent space models for grouped multiplex networks |
| Session CC438 | Room: K2.40 |
| Machine learning in applications | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Abdelaati Daouia | Organizer: CFE-CMStatistics |
| A0194: N. Ferreira, D. Aldea Mendes, V. Mendes | |
| Can higher data frequency lead to more accurate stock market predictions: Nasdaq 100 and DAX cases | |
| A1055: E. Cabana Garceran del Vall | |
| Enhancing sign language translation with real-time AI technology | |
| A1342: A. Alsayed | |
| EEMD-ELN regression for multi-scale relationships: Application for rainfall prediction | |
| A1588: I. Sousa | |
| Regression trees for analyzing longitudinal health data streams: A comparative study |
| Session CC416 | Room: S0.03 |
| Statistical modelling | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Jan Gertheiss | Organizer: CFE-CMStatistics |
| A0329: P. Vidoni | |
| A dynamic extension of the Massey's rating system with an application in basketball | |
| A1607: T. Besbeas, D. Stroungis | |
| On the choice of parametric link for binomial-response generalized linear models | |
| A1633: V. Piperigou | |
| On some bivariate transmuted distribution models | |
| A1487: R. Higashiguchi, K. Fukushima, K. Okada | |
| A cognitive diagnostic model for matching format tests |
| Session CC458 | Room: BH (SE) 2.09 |
| Macroeconometrics | Sunday 15.12.2024 13:40 - 15:20 |
| Chair: Masayuki Hirukawa | Organizer: CFE-CMStatistics |
| A0519: D. Koursaros | |
| The macroeconomic effects of internal promotions | |
| A1543: N. Hara | |
| Noisy past and business cycles | |
| A0180: M. Santi | |
| A high-dimensional GDP-at-risk and inflation-at-risk for the Euro area | |
| A1636: L. Fanelli, G. Angelini, G. Cavaliere | |
| A test of exogeneity in structural vector autoregressions with external instruments |
| Parallel session I: CFECMStatistics2024 | Sunday 15.12.2024 | 15:50 - 17:30 |
| A0153: H. Haupt | |
| Changes in precipitation: Trends, seasons, breaks, and memory | |
| A0154: G. Kauermann | |
| Statistical contributions in conflict research | |
| A0155: W. Semmler | |
| Climate risks and multiple objectives decision-making: Model-guided and empirical assessments |
| Session CO060 | Room: K0.16 |
| Advances in longitudinal studies | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Sanjoy Sinha | Organizer: Sanjoy Sinha |
| A0340: A. Oyet, B. Sutradhar | |
| Estimation of dynamic Logit mixed models for multinomial responses with categorical covariates | |
| A0748: P. Hu | |
| Leveraging longitudinal data for enhanced survival analysis using a novel deep transformer model | |
| A1619: M. Schnitzer, D. Berger, Y. Liu, D. Benkeser | |
| Nonparametric treatment model smoothing under sparsity for causal inference with longitudinal treatments | |
| A0333: S. Sinha | |
| Joint analysis of longitudinal count and binary data with outliers |
| Session CO288 | Room: K0.18 |
| Advances in minimax optimality | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Cecile Durot | Organizer: Cecile Durot |
| A0481: G. Chagny, A. Meynaoui, A. Roche | |
| Minimax estimation in the functional regression model with a functional output | |
| A0485: M. Sart | |
| On optimal adaptive estimation of a density | |
| A0567: C. Aaron | |
| Minimax geometric inference: Open and closed problems | |
| A1043: D. Mukherjee, C. Durot | |
| Minimax optimal rates of convergence in the shuffled and unlinked regression, and deconvolution under vanishing noise |
| Session CO207 | Room: K0.19 |
| Latest trends in clustering and classification of complex data I | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Marta Nai Ruscone | Organizer: Daniel Fernandez, Marta Nai Ruscone |
| A1426: J. Thompson, D. Krasnov | |
| Kernel metric learning for mixed-type fuzzy clustering | |
| A1439: S. Michael, A. Simpson | |
| Statistical few-shot learning via parameter pooling | |
| A1465: Y. Melnykov | |
| On the use of contaminated Gaussian distributions for modeling heavy tails and outliers | |
| A1642: C. Saunders, J. Hanka, C. Giefer | |
| Rank-based strategies for clustering distributions of pairwise scores |
| Session CO130 | Room: K0.20 |
| Recent advances in quantile and M-quantile regression | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Luca Merlo | Organizer: Luca Merlo |
| A0626: J. Bhattacharya | |
| Uniform inference for two step quantile regression process | |
| A0796: G. Stupfler, M. Oesting | |
| Extreme conditional quantile estimation for location-scale regression models and time series | |
| A0986: F. Pantalone, M.G. Ranalli, N. Salvati, L. Petrella | |
| M-quantile regression shrinkage and selection via the Lasso and Elastic Net | |
| A0907: B. Foroni, L. Merlo, N. Salvati, L. Petrella | |
| Hidden Markov linear quantile graphical model |
| Session CO249 | Room: K0.50 |
| Subsampling and non-linear problems: New proposals in optimal design | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Chiara Tommasi | Organizer: Laura Deldossi, Chiara Tommasi |
| A0969: A. Cia-Mina, L. Deldossi, J. Lopez-Fidalgo, C. Tommasi | |
| Subdata selection for prediction under model misspecification | |
| A0660: R. Frieri, A. Baldi Antognini, M. Zagoraiou | |
| Optimal design for parameters estimation in generalized linear models with treatment-by-covariate interactions | |
| A0867: S. Dasgupta, H. Dette | |
| Subsampling and its advantages for exponential family models | |
| A0394: S. Pozuelo Campos, V. Casero-Alonso, J. Lopez-Fidalgo, C. Tommasi, W. Wong | |
| Optimal discrimination designs for a constrained type of random effects models |
| Session CO119 | Room: K2.40 |
| Frontiers in statistical network analysis | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Keith Levin | Organizer: Keith Levin |
| A0165: W. Zhou, T. Li, W. Du | |
| Detection and statistical inference on informative core and periphery structures in weighted directed networks | |
| A0271: A. Kreiss, E. Mammen, W. Polonik | |
| Modelling sparse influence networks with Hawkes process while controlling for global influence | |
| A0510: S. Chatterjee, S. Bhattacharyya, N. Hwang, J. Xu | |
| Estimation of the number of communities for sparse networks | |
| A1026: V. Lyzinski, K. Pantazis, M. Trosset, W. Frost, C. Priebe | |
| Optimizing the induced correlation in omnibus joint graph embeddings |
| Session CO319 | Room: K2.41 |
| Survival analysis: Censoring and competing risks | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Takeshi Emura | Organizer: Takeshi Emura |
| A0170: R. Wilke, M.S.S. Lo, S. Shi | |
| A copula duration model with dependent states and spells | |
| A0173: V. Gares, J.-F. Dupuy, V. Chezeu | |
| Survival analysis for matched health databases | |
| A0859: D. Dobler, M. Ditzhaus, M. Munko, D. Edelmann, S. Mack | |
| Effect measures for comparing consecutive survival times | |
| A1508: P. Gonzalez-Barquero, R. Lillo, A. Mendez-Civieta | |
| Variable selection in high-dimensional survival analysis |
| Session CO266 | Room: S0.03 |
| Recent developments in biostatistics and bioinformatics | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Li-Pang Chen | Organizer: Li-Pang Chen |
| Session CO023 | Room: S0.11 |
| Statistics in neuroscience II | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Jeff Goldsmith | Organizer: Jeff Goldsmith |
| A0443: K. Linn | |
| Algorithmic fairness of models for predicting Alzheimer's disease progression | |
| A0531: T. Nichols, J. Lefort-Besnard, C. Maumet | |
| Same data meta analysis: Inference on neuroimaging multiverse data | |
| A1157: M. Fiecas | |
| Changepoint analysis in a mixed model framework, with applications to fMRI time series | |
| A1156: Y. Guo | |
| Investigating brain functional connectome using regularized blind source separation |
| Session CO256 | Room: S0.12 |
| Extreme value theory for environmental applications | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Juliette Legrand | Organizer: Juliette Legrand |
| A0297: T. Ahmad, C. Gaetan, P. Naveau | |
| An extended generalized Pareto regression model for count data | |
| A0474: J. Richards, C. Murphy-Barltrop, R. Majumder | |
| A deep geometric approach to modelling multivariate extreme | |
| A0633: E. Simpson, P. Northrop | |
| Block maxima modelling in the presence of missing data | |
| A0744: G. Toulemonde, A. Boulin, E. Di Bernardino, T. Laloe | |
| Spatial clustering of multivariate time series based on extremal dependence between sites |
| Session CO152 | Room: S0.13 |
| Recent advances in Statistics | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Trambak Banerjee | Organizer: Monika Bhattacharjee |
| A0428: T. Banerjee | |
| Harnessing the collective wisdom: Fusion learning using decision sequences from diverse sources | |
| A1667: P. Majumder, S.M. Siuli Mukhopadhyay | |
| Sample size requirements to detect an intervention by time interaction in four-level longitudinal CRT | |
| A1668: S. Ghosh | |
| A family of nonparametric tests for DMTTF alternatives based on moment inequality | |
| A0828: H. Busshoff | |
| Model multiplicity in policy learning |
| Session CO162 | Room: S-1.01 |
| HiTEc: Topics in financial econometrics | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Alessandra Amendola | Organizer: Vincenzo Candila, Alessandra Amendola |
| A0494: F. Spagnolo | |
| On periods of extreme asset price volatility to signal the beginning of a recession | |
| A0690: S. Stalder, F. Audrino, J. Gentner | |
| Quantifying uncertainty: A new era of measurement through large language models | |
| A0988: A. Amendola, V. Candila, P. Winker, S. Dehghan Jabarabadi | |
| Does sustainability impact tail risk measurement: Evidence from a novel text-based ESG indicator | |
| A1142: G. Bonaccolto, M. Caporin, J. Shahzad | |
| (Quantile) spillover indexes: Simulation-based evidence, confidence intervals and a decomposition |
| Session CO254 | Room: S-1.04 |
| Estimation for jump process | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Marie du Roy de Chaumaray | Organizer: Marie du Roy de Chaumaray |
| A0555: E. Bayraktar, E. Clement | |
| Estimation of a pure-jump stable Cox-Ingersoll-Ross process | |
| A0870: C. Amorino, A. Gloter, C. Dion, S. Sarah Lemler | |
| Invariant density estimation of self-exciting jump-diffusion | |
| A1482: T.B.T. Ngo, A. Brouste, L. Denis | |
| Efficient estimation of stable-Levy SDEs with constant scale coefficient | |
| A1557: R. Lacoste, C. Denis, C. Dion, L. Sansonnet, Y. Bas | |
| Bats monitoring: A classification procedure of bats behaviors based on Hawkes processes |
| Session CO171 | Room: S-1.06 |
| Inference of complex stochastic dynamic models | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Danna Zhang | Organizer: Danna Zhang |
| A0643: J. Li, J. Schmidt-Hieber, W.B. Wu | |
| Online inference for stochastic gradient descent with dropout regularization | |
| A0910: M. Xu | |
| Uncertainty quantification with a latent variable model | |
| A0951: Y. Han | |
| Tensor-augmented transformers for multi-dimensional time series forecasting | |
| A1004: D. Zhang | |
| Linear discriminant analysis of high-dimensional time series |
| Session CO273 | Room: S-1.27 |
| Advances in analyzing high dimensional data | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Hossein Moradi Rekabdararkolaee | Organizer: Hossein Moradi Rekabdararkolaee |
| Session CO015 | Room: S-2.23 |
| Anomalies in asset pricing (virtual) | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Nathan Lassance | Organizer: Nathan Lassance |
| A0167: N. Lassance, A. Martin-Utrera | |
| Limits to arbitrage to explain portfolio gains from asset mispricing | |
| A0182: M. Velikov, V. Azevedo, C. Hoegner | |
| The expected returns on machine-learning strategies | |
| A0500: P. Barroso, H. Wang | |
| Facts, momentum and factor momentum | |
| A1118: A. Lopez Lira | |
| Why do asset pricing models fail in equilibrium |
| Session CO332 | Room: S-2.25 |
| Integrative analysis of multi-source and multi-way data | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Thierry Chekouo | Organizer: Eric Lock |
| A0772: A. Tenenhaus, M. Tenenhaus | |
| A non-iterative algorithm for structural equations modeling | |
| A0840: L. Guan | |
| Partially characterized topology guides reliable anchor-free scRNA-integration | |
| A0862: K. Van Deun | |
| Latent variable methods for multi-view high-dimensional data | |
| A0994: S. Safo | |
| Multi-view multivariate mediation analysis |
| Session CO347 | Room: BH (S) 2.01 |
| Econometrics applied to finance and insurance | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Masayuki Hirukawa | Organizer: Masayuki Hirukawa |
| A0757: C. Salvagnin, A. Glielmo, M.E. De Giuli, A. Mira | |
| Nonparametric modelling of EUA market returns, volatility, and financial market links: A data-driven approach | |
| A0478: L. Merli, C. Tarantola, L. Dalla Valle, S.A. Osmetti | |
| Social media information to forecast Bitcoin value: A comparison of vines and graphical models | |
| A0861: S. Facchinetti, S.A. Osmetti, C. Tarantola | |
| Ordered response models for cyber risk assessment | |
| A1126: S. Song, K.-P. Lee | |
| CEOs on social media and stock market predictability |
| Session CO327 | Room: BH (S) 2.03 |
| Time series econometrics | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Josu Arteche | Organizer: Josu Arteche |
| A0291: P. Sibbertsen, T. del Barrio Castro, A. Escribano | |
| Modeling and forecasting the long memory of cyclical trends in paleoclimate data | |
| A0345: T. del Barrio Castro, P. Sibbertsen, A. Sanso | |
| Long memory in the marginalized time series of a VAR revisited | |
| A0518: J. Arteche, L.F. Martins | |
| Estimation of time-varying long memory series | |
| A0651: C. Velasco, I. Lobato | |
| Time domain estimation of non-fundamental ARMA models in the presence of heteroskedasticity of unknown |
| Session CO217 | Room: BH (S) 2.05 |
| Econometrics of art markets | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Douglas Hodgson | Organizer: Douglas Hodgson |
| A0242: D. Hodgson, D. Ackerberg | |
| Career profiles of design quality for golf course architects | |
| A0245: J. Woronkowicz, D. Noonan | |
| Estimating the value of psychic income for artists and other workers | |
| A0459: B. Coate, D. Hodgson | |
| A comparison of the career profiles of Australian Impressionist artists | |
| A1002: C. Hellmanzik, L. Kuld, S. Mitchell | |
| The "Motherhood Penalty" in artistic production: Historical evidence from American authors, 1800-1999 |
| Session CO369 | Room: BH (SE) 1.01 |
| Bayesian high-dimensional regression and model selection | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Somak Dutta | Organizer: Somak Dutta |
| A1206: V. Roy | |
| Informed MCMC for Bayesian variable selection | |
| A1207: J. Huggins | |
| Reproducible Bayesian model selection and high-dimensional regression | |
| A1455: A. Bhattacharya, D. Pati | |
| Non-asymptotic Laplace approximation under model misspecification | |
| A1464: H. Cheng | |
| Bayesian variable selection for multi-layer biological data |
| A0903: A. Sottosanti | |
| Bayesian mapping of mortality clusters | |
| A1063: D. Sinha | |
| Bayesian monotone single-index quantile regression model with bounded response and misaligned functional covariates | |
| A1109: R. Argiento, L. Paci, E. Filippi-Mazzola | |
| Model-based clustering of categorical data based on the Hamming distance | |
| A1692: S. Williamson | |
| Posterior uncertainty quantification in neural networks using data augmentation |
| Session CO296 | Room: BH (SE) 1.05 |
| Change-point detection for high-dimensional or non-Euclidean data | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Lynna Chu | Organizer: Lynna Chu |
| A0236: D. Wang, H. Xu, Z. Zhao, Y. Yu | |
| Change point inference in high-dimensional regression models under temporal dependence | |
| A0465: H. Song | |
| Practical and powerful kernel-based change-point detection | |
| A0622: R. Wang, X. Zhang, S. Chakraborty | |
| High-dimensional change-point detection using generalized homogeneity metrics | |
| A1077: L. Xie | |
| Integrating privacy enhancements with dynamic community detection |
| Session CO380 | Room: BH (SE) 1.06 |
| Modern seasonality and risk analysis | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Yushu Li | Organizer: Yushu Li |
| A1016: S. Hoelleland, H. Otneim, G.D. Berentsen, K. Fokianos | |
| Modern portfolio theory with seasonal assets | |
| A1361: E. Lamo | |
| Particle Markov chain Monte Carlo for parameter estimation in volatility models | |
| A1490: S. Westgaard | |
| Forecasting jointly value at risk and expected shortfall for energy commodities using quantile regression | |
| A1476: M. Villani | |
| Time-varying multi-seasonal AR models |
| Session CO012 | Room: BH (SE) 2.01 |
| High-dimensional data | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Nicola Loperfido | Organizer: Nicola Loperfido |
| A0240: T. Tarpey | |
| Precision nosology for mental health research | |
| A0243: B. Nadler, E. Azar | |
| Semi-supervised sparse Gaussian classification: Provable benefits of unlabeled data | |
| A0788: A. Bekker, M. Arashi, J. Van Niekerk, A. van Wyk | |
| Bayesian variable selection for skew normal models | |
| A1279: G. Piscopo, M. Longobardi, M. Giacalone | |
| Evaluation of the random match probability in forensic statistics |
| Session CO043 | Room: BH (SE) 2.09 |
| Applied macro-finance I | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Alessia Paccagnini | Organizer: Alessia Paccagnini |
| A1364: A. Casalis | |
| Paying for the prices: The cost of taming inflation | |
| A1379: V. Arcabic, T. Globan, G. Markusic | |
| Club convergence of real wages in the European Union | |
| A1478: M. Blix Grimaldi, S. Anyfantaki, C. Madeira, S. Malovana, G. Papadopoulos | |
| Climate risks and sovereign risks nexus | |
| A0211: P. Caraiani | |
| Oil shocks and firm-level expectations |
| Session CO025 | Room: BH (SE) 2.12 |
| Spatial statistics: Computer emulation, neuroscience \& ecology | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Rajarshi Guhaniyogi | Organizer: Rajarshi Guhaniyogi |
| A1171: E. Biswas, D. Nordman, M. Kaiser, A. Kaplan | |
| A bootstrap-based goodness of fit test for binary spatial models | |
| A1175: R. Gutierrez, R. Guhaniyogi, A. Scheffler | |
| Unraveling complex relationships between misaligned images with additive neural network Gaussian processes | |
| A1321: J. Bradley | |
| Exact MCMC-free Bayesian inference for data of any size | |
| A1640: D. Francom | |
| Comparing emulators systematically |
| Session CO059 | Room: Safra Lec. Theatre |
| Statistical methods for functional and high-dimensional data | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Eliana Christou | Organizer: Eliana Christou |
| A1069: D. Kowal, T. Sun | |
| Ultra-efficient MCMC for Bayesian longitudinal functional data analysis | |
| A1276: S. Wang, E. Christou, E. Solea, J. Song | |
| Dimension reduction for the conditional quantiles of functional data with categorical predictors | |
| A1283: B. Risk, Z. Wang, I. Gaynanova, A. Aravkin | |
| Sparse independent component analysis with an application to cortical surface fMRI data in autism | |
| A1641: T. Ogden, B. Shi, G. Matheson | |
| Functional nonlinear mixed-effects modeling of convolved data when direct observations are sparse |
| Session CO285 | Room: K2.31 (Nash Lec. Theatre) |
| Exploring new frontiers in causal mediation analysis | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Yi Zhao | Organizer: Honglang Wang |
| A0638: Q. Zhang | |
| Mediation analysis with high dimensional exposures and confounders | |
| A0802: F. Li, C. Cheng | |
| Semiparametric causal mediation analysis in cluster-randomized experiments | |
| A1075: Y. Zhao, Y. Xu, Y. Zhao | |
| Mediation analysis with graph mediator | |
| A1053: K. Rudolph | |
| Nonparametric mediation estimators that accommodate multiple mediators and multiple intermediate confounders |
| Session CO070 | Room: BH (S) 1.01 Lec. Theathre 1 |
| Advances in multivariate time series analysis | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Gianluca Cubadda | Organizer: Gianluca Cubadda, Alain Hecq |
| A0480: F. Giancaterini, G. Cubadda, S. Grassi | |
| A sequential Monte Carlo approach to estimate noncausal processes | |
| A0503: I. Ricardo | |
| Reduced-rank matrix autoregressive models: A medium N approach | |
| A1086: A. Hecq | |
| Green bubbles: A noncausal approach | |
| A1116: D. Velasquez-Gaviria, A. Hecq | |
| Exploring noncausal and noninvertible ARMA-GARCH dynamics in the cryptocurrency market |
| Session CC507 | Room: BH (SE) 2.05 |
| Computational and financial econometrics | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Antoine Djogbenou | Organizer: CFE-CMStatistics |
| A1717: A. Garcia | |
| High-dimensional covariance matrix estimators on simulated portfolios | |
| A1716: V. Sarafidis, G. Kapetanios | |
| Identification and estimation of network models using panel data analysis | |
| A1713: K. Natsiopoulos, N. Tzeremes | |
| Comparative evaluation of open-source and proprietary software for ARDL, EC models, and bounds test for cointegration | |
| A1722: S. Pollock | |
| Multidimensional arrays, indices and Kronecker products |
| Session CC474 | Room: BH (SE) 2.10 |
| Machine learning for economics and finance I | Sunday 15.12.2024 15:50 - 17:30 |
| Chair: Ines M del Puerto | Organizer: CFE-CMStatistics |
| A0774: L. Fluri | |
| Sparse neural networks and explainability in financial statement analysis | |
| A1310: Y. Chen, R. Calabrese | |
| Effects of Imbalanced Datasets on Algorithmic Fairness in Credit Scoring | |
| A1673: T. Ichiba | |
| Smoothness of directed chain stochastic differential equations and its applications | |
| A1387: J. C-Rella, J. Vilar Fernandez, R. Cao, D. Martinez Rego | |
| Reinforcement learning for credit risk |
| Parallel session J: CFECMStatistics2024 | Sunday 15.12.2024 | 17:40 - 18:55 |
| Session CO286 | Room: K0.16 |
| Network science in public health | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Thien Minh Le | Organizer: Thien Minh Le |
| A0742: R.P. Ghosh, J.-P. Onnela, I. Barnett | |
| A generalized estimating equation approach to network regression | |
| A0821: K. Zachrison | |
| Creating more equitable access to care through interhospital transfer networks | |
| A1311: T.M. Le | |
| Connecting mass-action models and network models for infectious diseases |
| Session CO244 | Room: K0.18 |
| Environmental data integration, estimation, and mapping | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Sara Franceschi | Organizer: Sara Franceschi, Natalia Golini, Michela Cameletti |
| A0426: A. Fusta Moro, J. Rodeschini, A. Moricoli, A. Fasso | |
| Air quality data fusion using fixed rank Kriging with estimates at municipal level | |
| A1112: G. Zoppi, N. Golini, R. Ignaccolo, A. Lo Presti, M. Cameletti | |
| Improving spatial maps with preferential sampling via hierarchical modeling | |
| A1194: A. Marcelli, R.M. Di Biase, S. Franceschi, M. Marcheselli, C. Pisani | |
| Species coverage estimation by means of Monte Carlo integration techniques |
| Session CO018 | Room: K0.19 |
| Latest trends in clustering and classification of complex data II | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Marta Nai Ruscone | Organizer: Daniel Fernandez, Marta Nai Ruscone |
| A1365: X. Zhu, A. Asilkalkan, S. Sarkar | |
| Finite mixture of hidden Markov models for tensor variate time series data | |
| A1463: V. Melnykov, L. Wang | |
| Finite mixture modeling for the analysis of spatiotemporal aspects in dendrochronology | |
| A1402: L. Sousa, I. Pereira, M. Monteiro | |
| Clustering time series of counts |
| Session CO397 | Room: K0.20 |
| Advances in robust prior elicitation | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Evan Kwiatkowski | Organizer: Ethan Alt |
| A0820: S. Calderazzo | |
| Robust external information borrowing in clinical trial hypothesis testing | |
| A1107: E. Kwiatkowski | |
| Case weighted power priors for hybrid control analyses with time-to-event data | |
| A1113: L. Egidi, R. Macri Demartino, N. Torelli, I. Ntzoufras | |
| Eliciting prior information from clinical trials via calibrated Bayes factor |
| Session CO033 | Room: K0.50 |
| Computational methods for design of experiments | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Vasiliki Koutra | Organizer: Vasiliki Koutra |
| A0882: K. Schorning | |
| Optimal designs for state estimation in networks | |
| A1166: P. Mozgunov | |
| Computationally efficient approach to operational prior specification for phase I dose-escalation trials | |
| A1510: E. Rowlinson, T. Waite | |
| A Laplace-based policy approach to sequential Bayesian design |
| Session CO102 | Room: K2.40 |
| Machine learning methods and their applications in biomedical data analysis | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Yi Li | Organizer: Yi Li |
| Session CO081 | Room: K2.41 |
| Analyzing high-dimensional data with network structure | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: George Michailidis | Organizer: Abolfazl Safikhani |
| A0761: A. Shojaie | |
| Semi-parametric inference for doubly stochastic spatial point processes | |
| A0863: G. Michailidis | |
| Joint learning of panel VAR models with low rank and sparse structure | |
| A1030: S. Basu | |
| A pathwise coordinate descent algorithm for penalized quantile regression |
| Session CO236 | Room: S0.03 |
| Recent advances in machine learning in econometrics | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Weining Wang | Organizer: Weining Wang |
| A1412: S. Han | |
| Policy learning with distributional welfare | |
| A1422: M. Xu, T. Otsu, K. Shinoda | |
| Semiparametric and nonparametric instrumental variable estimation with first-stage isotonic regression | |
| A1549: T. Kley, Y.P. Liu, H. Cao, W.B. Wu | |
| Change point analysis with irregular signals |
| Session CO113 | Room: S0.12 |
| New developments in nonparametric statistics and network analysis | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Joshua Cape | Organizer: Joshua Cape |
| A0260: H. Chen, X. Lin | |
| UBSea: A unified community detection framework | |
| A0411: D. Wang, Y. Khoo, Y. Peng | |
| Nonparametric estimation via variance-reduced sketching | |
| A1240: Z. Lubberts | |
| Euclidean mirrors and first-order changepoints in network time series |
| Session CO035 | Room: S0.13 |
| Modeling strategies for biomedical data | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Michelle Miranda | Organizer: Michelle Miranda |
| A0575: M. Miranda | |
| A fast Bayesian estimation of multi-subject fMRI activation patterns via a canonical polyadic tensor basis | |
| A0595: F. Nathoo, A. Yang, F. Hamilton, B. Nelson, J. Lum, M. Lesperance | |
| POI-SIMEX for conditionally Poisson distributed biomarkers from tissue microarrays | |
| A0639: Y. Shahhoseni, F. Nathoo, C. Beaulac, M. Miranda | |
| Bayesian spatial model finds association between ADHD medication and long memory properties of rs-FMRI in the cerebellum |
| Session CO351 | Room: S-1.01 |
| HiTEc: Model instabilities and data dependency | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Matus Maciak | Organizer: Michal Pesta, Matus Maciak |
| A0871: M. Maciak | |
| Exogenous and endogenous market effects: Model based change-point detection | |
| A1394: M. Pesta, M. Huskova | |
| Changing intensities | |
| A1399: B. Pestova, M. Pesta, M. Romanak | |
| Changepoint detection in tensor data |
| Session CO508 | Room: S-1.04 |
| Advances in data depth | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Stanislav Nagy | Organizer: Stanislav Nagy |
| A1723: S. Nagy, R. Dyckerhoff | |
| Halfspace depth for directional data | |
| A1725: E. Mendros, S. Nagy | |
| Explicit bivariate simplicial depth | |
| A1724: F. Bocinec, S. Nagy | |
| Concentration inequalities for location and scatter halfspace median under contaminated alpha-symmetric distributions |
| Session CO337 | Room: S-1.06 |
| Interpretable machine learning and high-dimensional statistics (virtual) | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Garvesh Raskutti | Organizer: Garvesh Raskutti |
| A0293: L. Zheng, A. Chang, G. Allen | |
| Cluster quilting: Spectral clustering for patchwork learning | |
| A1189: Y. Zhong | |
| Can large language models solve compositional tasks: A study of out-of-distribution generalization | |
| A1204: R. Dudeja, S. Liu, J. Ma | |
| Optimal iterative algorithms for structured PCA with invariant noise |
| Session CO262 | Room: S-1.27 |
| Study design and causal inference issues in complex biomedical studies | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Florin Vaida | Organizer: Florin Vaida |
| Session CO138 | Room: S-2.23 |
| Trustworthy AI (virtual) | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Wei Sun | Organizer: Wei Sun |
| A0251: X. Bi | |
| A plug-and-play watermark framework for AI-generated images | |
| A0905: R. Zhu, Z. Zhang, R. Qiu, Z. Yu | |
| Synergetic random forests for policy evaluation and uncertainty quantification in reinforcement learning | |
| A1070: C. Xu | |
| Sequential conformal prediction for time series |
| Session CO382 | Room: S-2.25 |
| Recent approaches in species distribution modelling (virtual) | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Maria Franco Villoria | Organizer: Maria Franco Villoria |
| A0768: A. Chakraborty | |
| Bayesian inference on high-dimensional multivariate binary responses | |
| A1010: L. Ferrari, M. Ventrucci | |
| Variance partitioning-based priors for species distribution models | |
| A1325: L. Altieri, D. Cocchi | |
| Auto-correlation-driven environmental sampling: an adaptive approach |
| Session CO032 | Room: Auditorium |
| Dynamic econometrics | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Esther Ruiz | Organizer: Esther Ruiz |
| A0178: E. Ruiz, D. Fresoli, P. Poncela | |
| Dealing with idiosyncratic cross-correlation when constructing confidence regions for PC factors | |
| A0198: F. Krabbe, L. Catania, A. Harvey | |
| A new approach to regime switching autoregressions | |
| A0479: A. Carnero, A. Leon, T. Niguez | |
| Skewness and kurtosis of aggregated financial returns |
| Session CO145 | Room: BH (S) 2.03 |
| Topics in financial econometrics | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Florian Richard | Organizer: Florian Richard |
| A1011: A. Brou, R. Luger | |
| The economic value of reward-to-risk timing strategies using return-decomposition GARCH models | |
| A1423: H. Tang, L. Khalaf | |
| Monetary policy surprises: Robust dynamic causal effects | |
| A1193: F. Richard, L. Khalaf | |
| Simulation-based multiple testing for many non-nested multivariate models |
| Session CO210 | Room: BH (S) 2.05 |
| Stochastic dominance and applications in finance | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Nikolas Topaloglou | Organizer: Nikolas Topaloglou, Stelios Arvanitis |
| A1105: A. Kofina, E. Passari, N. Topaloglou | |
| Are commodity markets segmented: Understanding cross-asset interdependencies using stochastic spanning | |
| A1181: N. Topaloglou, A. Kofina, I. Psaradelis | |
| On the existence of a true mutual fund factor model | |
| A1223: I. Psaradellis, N. Topaloglou | |
| ETFs, stochastic dominance and market efficiency |
| Session CO038 | Room: BH (SE) 1.01 |
| Bayesian methids for record linkage and small area estimation | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Jairo Fuquene | Organizer: Jairo Fuquene |
| A1285: H. Butler, A. Kaplan | |
| Genealogical application of record linkage for black Americans in the Antebellum South | |
| A1280: X. Tang | |
| A hierarchical gamma prior for modeling random effects in small area estimation | |
| A1516: S. Watakajaturaphon | |
| Bayesian alternatives to model the variances of direct estimates |
| A0409: M. Daniels, S. Haneuse, D. Lindberg | |
| A Bayesian nonparametric approach for nonignorable missingness in EHR data | |
| A0637: S. Garelli | |
| Bayesian inference via predictive distributions | |
| A1691: R. Ryder | |
| A phylogenetic model of the evolution of discrete matrices for the inference of lexical \& phonological language history |
| Session CO211 | Room: BH (SE) 1.05 |
| Machine learning and Bayesian methods in finance | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Martina Zaharieva | Organizer: Martina Zaharieva |
| A0659: C. Ausin, M. Kalli | |
| Modelling extreme joint dependence using Bayesian nonparametric copulas | |
| A0898: C. McKee, M. Kalli | |
| Mutually dependent Bernoulli processes for multivariate change-point detection | |
| A0964: I. Martins | |
| What events matter for exchange rate volatility |
| Session CO366 | Room: BH (SE) 1.06 |
| Recent advances on nonparametric panel data analysis | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Juan Manuel Rodriguez-Poo | Organizer: Juan Manuel Rodriguez-Poo |
| A0383: D. Henderson, A. Soberon, T. Wang, S. Ghazi | |
| Labor income tax shocks and corporate innovation | |
| A0405: A. Soberon, J.M. Rodriguez-Poo, D. Henderson, T. Wang | |
| Estimation of functional coefficient panel data models with sample selection and fixed effects: A pairwise approach | |
| A0487: S. Sperlich, J.M. Rodriguez-Poo, A. Soberon | |
| Estimation and inference of panel data models with a generalized factor structure | |
| A0515: L.A. Arteaga Molina, J.M. Rodriguez-Poo | |
| An empirical likelihood goodness-of-fit test for panel data models with interactive fixed effects |
| Session CO006 | Room: BH (SE) 2.01 |
| Projection pursuit | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Nicola Loperfido | Organizer: Nicola Loperfido |
| A0560: A. Berti, N. Loperfido, C. Franceschini | |
| Bayesian projection pursuit for efficient and sustainable banking | |
| A0609: C. Franceschini, N. Loperfido, N. Loperfido | |
| Projection pursuit for art analysis | |
| A0872: C. Adcock | |
| Projection pursuit and portfolio selection |
| Session CO118 | Room: BH (SE) 2.05 |
| New challenges for statistical process control | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Claudio Giovanni Borroni | Organizer: Manuela Cazzaro, Claudio Giovanni Borroni |
| A1060: P. Qiu | |
| Control charts for dynamic process monitoring with an application to air pollution surveillance | |
| A0777: M. Cazzaro, C.G. Borroni, P.M. Chiodini | |
| Comparing the treatment of old data in some self-starting control charts | |
| A1263: C.G. Borroni, M. Cazzaro | |
| Extending a novel class of control charts for sequential monitoring to the multivariate framework |
| Session CO009 | Room: BH (SE) 2.09 |
| Applied macro-finance II | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Alessia Paccagnini | Organizer: Alessia Paccagnini |
| A1531: A. Paccagnini | |
| The economic superpower: Analyzing the impact of superhero movies on the U.S. business cycle | |
| A1599: A. Cipollini, I. Lo Cascio, F. Parla, F. Parla | |
| How does the US stock market react to climate concern shocks across frequencies: A wavelet analysis | |
| A1643: L. Jackson Young, M. Owyang, A. Paccagnini | |
| The distributional effects of stabilization policy |
| Session CO124 | Room: BH (SE) 2.10 |
| Recent advancements in modern multivariate problems | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Chenlu Ke | Organizer: Chenlu Ke |
| A0825: H. Moradi Rekabdararkolaee | |
| Dimension reduction for spatially correlated data | |
| A1220: X. Kong | |
| Multivariate rank-based expectation of the conditional difference for testing independence | |
| A1079: Y. Li | |
| Some modeling considerations involving the exponentially-modified Gaussian (EMG) distribution |
| Session CO090 | Room: BH (SE) 2.12 |
| Statistical methods for analyzing big and complex data (virtual) | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Trambak Banerjee | Organizer: Trambak Banerjee |
| A0355: N. Chakraborty, A. Bhattacharya, S. Lahiri | |
| Statistical inference for subgraph densities under induced random sampling from network data | |
| A1467: P. Sharma | |
| Detecting structural changes in time varying parameters of panel models | |
| A1470: S. Parsaeian | |
| Latent group structures and sparsity analysis in high dimensional panel MIDAS models |
| Session CO062 | Room: Safra Lec. Theatre |
| Advanced topics in functional and object data analysis (virtual) | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Kuang-Yao Lee | Organizer: Kuang-Yao Lee |
| A0502: S. Bhattacharjee, H.-G. Mueller | |
| Geodesic mixed effects models for repeatedly observed/longitudinal random objects | |
| A0589: Y. Zhou, S.I. Iao, H.-G. Mueller | |
| Deep Frechet regression | |
| A1261: D. Lin, L. Xue, B. Li | |
| Structure-preserving nonlinear sufficient dimension reduction for scalar-on-tensor regression |
| Session CO195 | Room: K2.31 (Nash Lec. Theatre) |
| Machine learning-based fairness and causal estimation | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Ashkan Ertefaie | Organizer: Ashkan Ertefaie |
| A0396: D. Benkeser | |
| Estimation of constrained statistical functionals for fair machine learning | |
| A0915: I. Malenica | |
| Kernel debiased plug-in estimation | |
| A1068: I. Diaz | |
| General targeted machine learning for modern causal mediation analysis |
| Session CO198 | Room: BH (S) 1.01 Lec. Theathre 1 |
| Nonlinear and non-Gaussian time series | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Sean Telg | Organizer: Sean Telg |
| A1290: K. Cecere Palazzo, S. Telg, S.J. Koopman, F. Blasques | |
| A panel extension for noncausal models | |
| A1571: S. Telg, S.J. Koopman, F. Blasques, G. Mingoli | |
| A novel test for the presence of local explosive dynamics | |
| A1451: S. Kooiker | |
| Nonparametric time-varying Granger causality using exponentially smoothed density estimators |
| Session CC424 | Room: S0.11 |
| Computational statistics | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Andreas Artemiou | Organizer: CFE-CMStatistics |
| A1496: J. Aubray, F. Nicol | |
| Polynomial regression on SE(3) with an arbitrary connection | |
| A1659: C. Gatu, G.-E. Pascaru, P.S. Drumia, E. Kontoghiorghes | |
| Combinatorial strategies for greedy regression model selection | |
| A1384: D. Ham, A. Rothman, B. Price | |
| New computational methods for multivariate regression |
| Session CC471 | Room: BH (S) 2.01 |
| Financial and econometric modelling | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Michail Karoglou | Organizer: CFE-CMStatistics |
| A0377: W.-C. Miao | |
| Using regression to enhance an existing closed-form implied volatility formula to widen the range of option moneyness | |
| A1581: M. Tadi, J. Witzany | |
| Copula-based trading of cointegrated cryptocurrency pairs | |
| A1284: D. Ammon, T. Hartl, R. Tschernig | |
| The specification of a fractionally integrated factor model |
| Session CC434 | Room: BH (S) 2.02 |
| Portfolio management | Sunday 15.12.2024 17:40 - 18:55 |
| Chair: Lorenzo Mercuri | Organizer: CFE-CMStatistics |
| A0623: A. Insana, V. Guidetti | |
| Social choice theory and market anomalies for portfolio construction | |
| A1341: M. Sahamkhadam, A. Stephan | |
| Multiobjective ESG bond portfolio optimization | |
| A1360: S. Muhinyuza, S. Mazur | |
| Estimation of risk aversion coefficient for tangency portfolio |
| Parallel session M: CFECMStatistics2024 | Monday 16.12.2024 | 09:10 - 10:50 |
| Session CI050 (Special Invited Session) | Room: Auditorium |
| Bayesian methods and applications | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Mario Peruggia | Organizer: Mario Peruggia |
| A0156: A. Lijoi, C. Del Sole, I. Pruenster | |
| A Bayesian nonparameteric approach to competing risks | |
| A0157: S. MacEachern | |
| Near-Bayesian methods | |
| A0158: M. Steel, G. Zens | |
| Model uncertainty in latent Gaussian models with univariate link function |
| Session CO086 | Room: K0.18 |
| Impactful spatio-temporal statistical applications | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Peter Craigmile | Organizer: Peter Craigmile |
| A0192: P. Otto, O. Dogan, S. Taspinar | |
| A dynamic spatiotemporal stochastic volatility model with an application to environmental risks | |
| A0569: L. Kakampakou, J. Wadsworth | |
| Spatial extreme value modelling via a geometric approach | |
| A0592: S. Baugh | |
| Quantifying causal relationships from climate observations using spatiotemporal stochastic interventions | |
| A0524: T. Smith, M. Basson, T. Louw | |
| Gaussian process models for pollution in rivers |
| Session CO084 | Room: K0.19 |
| New approaches and applications of spatial statistics | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Nicoletta D Angelo | Organizer: Andrea Gilardi, Nicoletta D Angelo |
| A0363: B. Begu | |
| A roughness penalty approach for time-evolving occurrences on planar and curved regions | |
| A0556: L. Aiello, L. Paci, R. Argiento, F. Finazzi | |
| Survival modelling of smartphone trigger data for earthquake parameter estimation in early warning | |
| A0784: G. Panunzi | |
| Estimating rare species distribution with opportunistic data: The case of the white shark in the Mediterranean Sea | |
| A0944: L. Patelli, M. Cameletti, M. Figueira Pereira | |
| SPDE-Forest: An hybrid approach for modeling geostatistical data |
| Session CO383 | Room: K0.20 |
| Data depth for complex data types and applications | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Pavlo Mozharovskyi | Organizer: Pavlo Mozharovskyi |
| A0373: M. Vimond, P. Mozharovskyi, P. Lafaye de Micheaux | |
| Data depth for probability measures | |
| A0701: A. Castellanos, P. Mozharovskyi, H. Janati | |
| Halfspace depth as a classification loss: A machine learning viewpoint on statistical data depth | |
| A0747: G. Francisci, C. Agostinelli, A. Nieto-Reyes, A. Vidyashankar | |
| Local depth functions and clustering | |
| A0937: I. Cascos, A. Grane Chavez, J. Qian | |
| MDS-based depth for mixed-type data applied to the assessment of biological age |
| Session CO203 | Room: K0.50 |
| Design of experiments and applications | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Stella Stylianou | Organizer: Stella Stylianou, Stelios Georgiou |
| A1606: O. Alhelali | |
| Sliced designs for computer experiments using sequences with zero autocorrelation function | |
| A1555: T. Dharmaratne, A. De Livera, S. Georgiou, S. Stylianou | |
| Supersaturated design-based statistical methods for variable selection in high-dimensional observational data | |
| A1611: D. Athanasaki | |
| Enhancing response surface methodology with Latin hypercube sampling techniques | |
| A1664: N. Alshammari, S. Georgiou, S. Stylianou | |
| Computational construction of sequential efficient designs for the second order model |
| Session CO136 | Room: K2.40 |
| Statistical sequential methods for decision-making problems | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Matteo Borrotti | Organizer: Matteo Borrotti |
| A0542: S. Han, K. Mylona, S. Gilmour | |
| Optimal subsampling for hierarchical data | |
| A0488: V. Zangirolami | |
| Enhancing data efficiency in online deep reinforcement learning under partial observability | |
| A0615: J. Zhu | |
| Sequential knockoffs for variable selection in reinforcement learning | |
| A0855: C. Shi | |
| Optimal design for A/B testing in time series experiments |
| Session CO173 | Room: K2.41 |
| Survival and longitudinal data analysis | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Din Chen | Organizer: Din Chen |
| Session CO154 | Room: S0.11 |
| Recent development of statistical methods for handling complex data | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Sollie Millard | Organizer: Weixin Yao |
| A0193: Z. Zeng | |
| Thresholding-based robust estimation for generalized mixture models | |
| A0285: C. Wang | |
| Semiparametric inference on inequality measures with nonignorable nonresponse | |
| A1617: D. Hofmeyr | |
| Predictive modelling with ensembles of projected nearest neighbors | |
| A0815: S. Millard, S. Millard, F. Kanfer, A. Kleynhans | |
| Robust model selection in mixture regression |
| Session CO066 | Room: S0.12 |
| Recent developments in clustering for complex data structure | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Maria Brigida Ferraro | Organizer: Monia Ranalli |
| A0672: L. Testa, T. Boschi, J. Di Iorio, M. Cremona, F. Chiaromonte | |
| COVID-19 in Italy: Contrasting pre-vaccine epidemic waves through functional data clustering | |
| A0758: M. Nai Ruscone, D. Fernandez, K. Preedalikit, L. McMillan, I. Liu, R. Costilla | |
| Mixture-based clustering with covariates for ordinal responses | |
| A0807: F. Amato, J. Jacques | |
| Clustering longitudinal mixed data | |
| A0995: C. Di Nuzzo, G. Zaccaria | |
| A fuzzy spectral clustering model |
| Session CO129 | Room: S0.13 |
| Recent advances in statistical modeling for medical and social data | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Tiejun Tong | Organizer: Tiejun Tong |
| A0330: K.Y. Wong, Q. Zhou | |
| An optimal two-step estimation approach for two-phase studies | |
| A0296: W. Su, W. Cui, X. Yan, X. Zhao | |
| Demographic parity-aware individualized treatment rules | |
| A1143: B. Jiang | |
| A hybrid model for zero-inflated proportion data | |
| A0258: C. Ma | |
| Heterogeneous longitudinal structural equation modeling and variable selection |
| Session CO193 | Room: S-1.06 |
| Biostatistics and machine learning: Benchmarking, evaluation and beyond | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Roman Hornung | Organizer: Roman Hornung |
| A0684: S. Szymczak | |
| The selection and creation of benchmark data sets for comparison studies: Challenges and solutions | |
| A0582: H. Schulz-Kuempel | |
| Evaluating model performance through confidence intervals for the generalization error | |
| A0917: M. Wuensch, M. Herrmann, E. Noltenius, M. Mohr, T. Morris, A.-L. Boulesteix | |
| On the handling of method failure in comparison studies | |
| A0979: R. Hornung, A. Hapfelmeier | |
| Multi forests: Variable importance for multi-class outcomes |
| Session CO225 | Room: S-1.27 |
| Statistical and machine learning in engineering | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Jan Gertheiss | Organizer: Jan Gertheiss |
| A0539: L. Neumann, P. Wittenberg, M. Koehncke, A. Mendler, S. Kessler, J. Gertheiss | |
| Monitoring of confounder-adjusted scores using conditional principal component analysis | |
| A0813: F. Vogel | |
| Functional quantile analysis for sensor outputs in structural health monitoring | |
| A1302: M. Jones, D. Pitchforth, E. Cross | |
| Deriving Gaussian processes for physics-informed structural health monitoring | |
| A1344: A. Hughes, E. Cross, K. Worden, S. Gibson, T. Rogers, M. Jones | |
| Quantifying the value of domain knowledge in physics-informed machine learning |
| Session CO282 | Room: S-2.25 |
| Methods and models for environmental and ecological data I | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Domenico Vitale | Organizer: Domenico Vitale |
| A0384: D. Di Cecco, A. Tancredi | |
| A Bayesian parametric approach to estimate misidentification errors in capture-recapture | |
| A0417: L. Scaccia, P. Rivadeneyra, L. Salvati | |
| Estimating the causal effect of glyphosate aspersion on coca cultivation in Colombia | |
| A0606: E. Rosci, E. Ceccarelli, G. Jona Lasinio, G. Minelli, M. Stafoggia | |
| A model-based approach for evaluating exposure to extreme events in public health: A case study on the Lazio region | |
| A0692: M. Ventrucci, M. Franco Villoria, L. Ferrari, A. Laini | |
| Analyzing community ecology metabarcoding data using variance partitioning methods |
| Session CO123 | Room: BH (S) 2.01 |
| Advances in quantitative risk management | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Hideatsu Tsukahara | Organizer: Hideatsu Tsukahara |
| A0238: T. Koike, C.W.-S. Chen, E.M.-H. Lin | |
| Forecasting and backtesting gradient allocations of expected shortfall | |
| A0403: T. Yoshiba, K. Ito | |
| Dynamic asymmetric tail dependence among multi-asset classes for portfolio management: Dynamic skew-t copula approach | |
| A0472: A. Dias | |
| Maximum pseudo-likelihood estimation of copula models and moments of order statistics | |
| A0694: A. Moura | |
| The role of dependences and the moral hazard constraint in optimal reinsurance |
| Session CO404 | Room: BH (S) 2.02 |
| Empirical and computational methods for finance | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Matthias Fengler | Organizer: Diego Ronchetti, Matthias Fengler |
| A0619: H. Jiang, O. Okhrin, M. Rockinger | |
| Artificial neural network small-sample-bias-corrections of the AR(1) parameter close to unit root | |
| A1153: S. Feistle, M. Fengler, A. Melnikov | |
| A non-Gaussian, structure-preserving stochastic volatility and option pricing model in discrete time | |
| A1363: M. Puke, K. Schweikert | |
| Coherent forecasting of realized volatility | |
| A1493: D. Ronchetti | |
| Ex-ante risk timing |
| Session CO344 | Room: BH (S) 2.03 |
| Causal inference in sustainable investing | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Serge Darolles | Organizer: Serge Darolles, Gaelle Le Fol |
| A0661: G. Le Fol, S. Darolles, Y. He | |
| Understanding the ESG score effects on stock returns using mediation theory | |
| A0735: G. Coqueret, T. Giroux, B. Qiu | |
| An anatomy of decarbonizing firms | |
| A0888: H. Mathurin | |
| Climate regulatory risk and technological opportunities | |
| A0663: J. Coadou, S. Darolles | |
| Betting against sustainability: Evidence from US equity short selling activity |
| Session CO384 | Room: BH (S) 2.05 |
| Latent variable modelling | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Roberto Casarin | Organizer: Roberto Casarin |
| A0202: A. Peruzzi, R. Casarin, M. Steel | |
| Media bias and polarization through the lens of a Markov switching latent space network model | |
| A0725: Z. Wang, M. Kalli | |
| Spatial heterogeneity in the effects of covariates on the distribution of income using Bayesian nonparametric methods | |
| A0853: A. Trovato, R. Casarin, D. Raggi | |
| A data-rich yield curve factor model | |
| A1601: G. Kastner, L. Gruber, M. Iacopini | |
| Sparse dynamic Bayesian graphical models |
| Session CO393 | Room: BH (SE) 1.01 |
| Bayesian applied econometrics | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Kazuhiko Kakamu | Organizer: Kazuhiko Kakamu |
| A0709: Y. Yamauchi, G. Kobayashi, S. Sugasawa | |
| General Bayesian quantile regression of count via generative modeling | |
| A0790: H. Nishino, K. Kakamu | |
| Bayesian model averaging for income distributions | |
| A0921: Y. Kawakubo, K. Kakamu | |
| Estimating spatial decomposition of income inequality via constrained Bayes method | |
| A0275: K. Kakamu | |
| Bayesian analysis of aging and declining household size on income distribution in Japan |
| Session CO139 | Room: BH (SE) 1.02 |
| Recent advances in symbolic data analysis | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Andrej Srakar | Organizer: Andrej Srakar, S Yaser Samadi |
| A0489: L. Billard | |
| Covariance estimation for histograms using copulas | |
| A1009: A. Sadeghkhani | |
| Interval-valued models in frequentist and Bayesian schemes | |
| A1006: S.Y. Samadi | |
| Multivariate interval-valued time series data analysis | |
| A1197: B. Beranger, S. Sisson, P. Rahman, A.H. Jamaluddin | |
| Advances in data analysis using aggregated data |
| Session CO489 | Room: BH (SE) 1.06 |
| Complex statistical methods for energy poverty related issues | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Alfonso Carfora | Organizer: Giuseppe Scandurra, Alfonso Carfora |
| A0691: L. Santoro | |
| How economic backwardness and institutional quality affect energy poverty: Evidence from Italian regional panel data | |
| A0805: L.F. Minervini, A. Carfora | |
| Assessing the targeting effectiveness of fiscal incentives toward energy-poor families | |
| A0845: X. Wang, L. Delina, K. Matus, Y. Qiu | |
| Still using solid fuels: Energy poverty in urban areas with different fuel use patterns | |
| A0849: G. Scandurra, C. Camporeale | |
| Explaining SDGs: How predict energy poverty in Italian households | |
| A1111: D. Dokupilova | |
| On a fair definition of energy poverty | |
| A0568: I. abdallah, L. Scaccia | |
| The path towards sustainability in the European Union countries: The role of the renewable energy policies |
| Session CO248 | Room: BH (SE) 2.01 |
| Recent advances in sample selection models | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Francisco Javier Rubio | Organizer: Emmanuel Ogundimu, Francisco Javier Rubio |
| A0612: W. Barreto-Souza, F. Souza, M. Genton | |
| A generalized Heckman model with varying sample selection bias and dispersion parameters | |
| A0641: A. Iqbal, E. Ogundimu, F.J. Rubio | |
| Bayesian variable selection in sample selection models using spike-and-slab priors | |
| A0868: E. O Neill | |
| Type II Tobit sample selection models with Bayesian additive regression trees | |
| A0274: F.J. Rubio, A. Iqbal, E. Ogundimu | |
| Model selection under sample selection and model misspecification |
| Session CO144 | Room: BH (SE) 2.05 |
| Empirical analysis of climate change | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Marina Friedrich | Organizer: Marina Friedrich |
| A0401: L. Emediegwu | |
| Global climate anomalies and economic welfare: Evidence from panel of US counties | |
| A0587: M. Gittard | |
| Droughts, migration and population in Kenya | |
| A0593: A. Phella, V. Gabriel, L.F. Martins | |
| Common persistent cycles | |
| A0965: M. Friedrich, Y. Shapovalova, K. Moussa, D. van der Straten | |
| Forecasting the atmospheric ethane burden above the Jungfraujoch with Bayesian and frequentist methods |
| Session CO039 | Room: BH (SE) 2.09 |
| Tweets, inflation and macroeconomic policies | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Etsuro Shioji | Organizer: Etsuro Shioji |
| A0676: T. Sekine | |
| How did people tweet against inflation in Japan | |
| A0787: H. Morita | |
| New approach to estimating the productivity of public capital: Evidence from 22 OECD countries | |
| A1185: N. Soma | |
| State-dependency of fiscal price puzzle | |
| A0212: E. Shioji | |
| Public investment news shocks: A text-based index |
| Session CO359 | Room: BH (SE) 2.12 |
| Topics in panel data models and their applications | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Chaowen Zheng | Organizer: Chaowen Zheng |
| Session CO153 | Room: Safra Lec. Theatre |
| Recent advances in functional data analysis | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Maximilian Ofner | Organizer: Alexander Aue |
| A0220: S. Kuehnert, A. Aue, G. Rice, J. Vander Does | |
| An operator-level GARCH model | |
| A0228: Q. Fang, J. Chang, X. Qiao, Q. Yao | |
| On the modelling and prediction of high-dimensional functional time series | |
| A0664: A. Caponera, H. Yun, V. Panaretos | |
| Reproducing kernel approach to tomographic data | |
| A0756: E. Lila, K. Motwani, A. Shojaie, A. Rokem | |
| Heritability modeling of complex functional phenotypes |
| Session CO260 | Room: K2.31 (Nash Lec. Theatre) |
| Advances in causal inference | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Stathis Gennatas | Organizer: Abdul-Nasah Soale, Stathis Gennatas |
| A1023: W. Zhang | |
| Evaluating and utilizing surrogate outcomes in covariate-adjusted response-adaptive designs | |
| A1597: R. Phillips | |
| Evaluating and improving real-world evidence with targeted learning | |
| A0310: E. Tsyawo, B. Callaway | |
| Treatment effects in staggered adoption designs with non-parallel trends | |
| A0393: A.-N. Soale | |
| Inference in regression with latent clusters: A penalty-free approach | |
| A1084: G. Valdes | |
| Assessing causal effects of radiation toxicity on overall survival |
| Session CO305 | Room: BH (S) 1.01 Lec. Theathre 1 |
| Inference for dependent data | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Fabian Mies | Organizer: Fabian Mies |
| A0371: D. Kurisu, Y. Matsuda | |
| Series ridge regression for spatial data | |
| A0534: B.C. Boniece, J. Figueroa-Lopez, Y. Han | |
| On data-driven tuning for truncated realized variations | |
| A0734: I.V. Curato | |
| Mixed moving average field guided learning for spatiotemporal data | |
| A1320: F. Mies, J. Koehne | |
| Multiscale change detection for non-stationary time series |
| Session CC485 | Room: K0.16 |
| Networks and graphical models | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Andrew Wood | Organizer: CFE-CMStatistics |
| A1578: R. Rihtamo, J. Virta, H. Saarinen | |
| Portfolio selection with complex network analysis | |
| A1438: Y. Zhang | |
| A network approach to macroprudential buffers | |
| A1535: L.F. Melo Velandia, M.S. Ramirez-Gonzalez | |
| Quality, location, and coffee price returns: A high-dimensional CoVaR-copula network analysis | |
| A1499: L. Vogels, R. Mohammadi, M. Schoonhoven, I. Birbil | |
| A review in Bayesian structure learning in Gaussian graphical models |
| Session CC494 | Room: S0.03 |
| Analysis of categorical data | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Frederic Ferraty | Organizer: CFE-CMStatistics |
| A1127: S. Jolani | |
| Hierarchical imputation of categorical variables in the presence of systematically and sporadically missing data | |
| A1278: H. Okahara, K. Tahata | |
| Capturing asymmetric structures and separability in multivariate contingency tables based on f-divergence | |
| A1195: F. Bacchi, M.R. Ferrante, P.P. Calia | |
| Goodness of fit assessment of item response theory models for binary data | |
| A1323: K. Nakamura, T. Nakagawa, K. Tahata | |
| Orthogonal decomposition of probability tables with Aitchison geometry for symmetry assessment |
| Session CC491 | Room: S-1.01 |
| Text data and related topics | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Etienne Marceau | Organizer: CFE-CMStatistics |
| A1612: K. Waki, S. Yuki, H. Yadohisa | |
| Topic Model for multiple supervised information based on non-linear functions | |
| A1299: L. Kontoghiorghes, G. Kapetanios | |
| Time-varying weighted latent Dirichlet allocation | |
| A1390: Y. Tan, K. Nomura, K. Okada | |
| Application of latent semantic scaling to high-dimensional text data for personality assessment | |
| A0670: E.-J. Senn, F. Audrino | |
| The power of visuals: Using social media images for financial sentiment analysis |
| Session CC450 | Room: S-1.04 |
| Contributions in causal inference | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Massimo Cannas | Organizer: CFE-CMStatistics |
| A1268: M. Cai, P. Gao, H. Wang, H. Hara | |
| Causal discovery for linear acyclic models with gaussian noise using ancestral relationships | |
| A1317: R. Haschka | |
| Handling endogenous regressors in quantile regression models: Copula approach without instruments | |
| A1529: R. Yamashita, K. Tsubotani, K. Tanioka, H. Yadohisa | |
| Robust synthetic control method for data with outliers | |
| A0607: J. Schuettler, E. De Giorgi, C. Hirt | |
| Analyzing the impact of social media on the trading behavior of retail investors |
| Session CC455 | Room: BH (SE) 1.05 |
| Forecasting I | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Michael Owyang | Organizer: CFE-CMStatistics |
| A0835: S. Spavound, N. Kourentzes | |
| A framework for analyzing the cost-benefit tradeoff of training neural networks | |
| A1334: Y. Yamamoto | |
| Testing and quantifying economic resilience | |
| A1414: A. Wolny-Dominiak, T. Zadlo, A. Chwila, M. Hadas-Dyduch, T. Stachurski, M. Krzciuk | |
| Voting-based ex ante method for selecting strategy of the price characteristics prediction on real estate market | |
| A1561: H. Nyberg, V. Kuntze, S. Rauhala | |
| Similarity-based path forecasting of U.S. recession periods |
| Session CC500 | Room: BH (SE) 2.10 |
| Machine learning in economics and finance II | Monday 16.12.2024 09:10 - 10:50 |
| Chair: Yoosoon Chang | Organizer: CFE-CMStatistics |
| A0636: A. Santos, J.L. Esteves dos Santos, M. Biscaia Caleiras | |
| Calibrating option pricing models using neural networks and population-based optimization methods | |
| A1539: J. Witzany, M. Ficura | |
| Machine learning applications for the valuation of options on non-liquid option markets | |
| A1553: M. Ficura | |
| Machine learning for commodity futures pricing | |
| A1652: C.F.C. Chu, P.K.D. Chan | |
| Prediction of local extrema in financial time series with multiple timeframe extreme gradient boosting method |
| Parallel session P: CFECMStatistics2024 | Monday 16.12.2024 | 14:40 - 16:20 |
| Session CO399 | Room: K0.16 |
| Recent advancements in statistical network analysis and beyond | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Weijing Tang | Organizer: Weijing Tang |
| A0408: Y. Zhao | |
| Community detection with heterogeneous block covariance model | |
| A0846: Y. He | |
| Efficient analysis of latent spaces in heterogeneous networks | |
| A0946: P. MacDonald, E. Kolaczyk | |
| Errorfully observed Markov models for evolving networks | |
| A1249: T. Li | |
| The non-overlapping statistical approximation to overlapping group lasso |
| Session CO169 | Room: K0.18 |
| Developments in spatio-temporal modeling of health outcome data | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Andrew Lawson | Organizer: Andrew Lawson |
| A0421: P. Moraga | |
| Dengue nowcasting in Brazil by combining official surveillance data and Google Trends information | |
| A0706: M. Prates, T. Pacheco, R. Assuncao | |
| Latent archetypes of the spatial patterns of cancer | |
| A0603: G. Li, P. Diggle, M. Blangiardo | |
| Using wastewater data for COVID-19 surveillance in the post-pandemic era: A data integration approach | |
| A0720: J. Kim, A. Lawson | |
| A novel Bayesian spatiotemporal surveillance metric to predict emerging infectious disease high-risk clusters |
| Session CO082 | Room: K0.19 |
| Causal inference | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Massimo Cannas | Organizer: Bruno Arpino, Massimo Cannas |
| A0342: L. Hu, H. Joshi, E. Scott, F. Li | |
| A continuous-time joint marginal structural survival model for causal inferences about multiple intermittent treatments | |
| A0571: M. Silan, P. Belloni | |
| The comparison of MARMoT adjustment and template matching in a multiple treatment framework: A simulation study | |
| A0586: G. Demuru, M. Musio, P. Dawid | |
| Bounds and identification for the probability of causation in individual cases | |
| A0611: B. Arpino, D. Bellani | |
| Propensity score matching for cross-classified data structures |
| Session CO313 | Room: K0.20 |
| Statistical inference and estimation under dependence | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Rajarshi Mukherjee | Organizer: Sohom Bhattacharya |
| A0653: S. Mukherjee | |
| Statistical inference in Ising and Potts models | |
| A0588: M. Liang | |
| Classification under outcome misclassification: Reliability quantification and partial identification | |
| A0645: S. Li, K. Han, J. Mao, H. Wu | |
| Detecting interference in A/B testing with increasing allocation | |
| A0800: R. Mukherjee | |
| Statistical inference under dependent Gaussian mixture models |
| Session CO299 | Room: K0.50 |
| Advances in subsampling and order-of-addition experiments (virtual) | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Nicholas Rios | Organizer: Nicholas Rios |
| A1122: J. Wang | |
| Subsampling in transfer learning | |
| A1080: C.-C. Yang | |
| Sampling big data for model building using dimension reduction | |
| A1579: X. Zhang | |
| Modeling and designs for pairwise constrained order-of-addition experiments | |
| A1574: J. Zheng, N. Rios | |
| Exact designs for OofA experiments under a transition-effect model |
| Session CO223 | Room: K2.40 |
| Recent developments in non-Euclidean statistics | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Andrew Wood | Organizer: Andrew Wood |
| A0391: J. Kent, C.C. Taylor, N. Almasoud | |
| To the limit and beyond for the frequency modulated Mobius periodic regression model | |
| A0618: L. Maestrini, J. Scealy, F. Hui, A. Wood | |
| Local-global extrinsic regression on manifolds | |
| A0771: A. Wood, K. Bharath, H. Le | |
| Empirical likelihood on manifolds | |
| A1074: A. Kume, S. Preston, K. Bharath, P. Lopez-Custodio | |
| A rolled Gaussian process model for curves on manifolds |
| Session CO377 | Room: K2.41 |
| Evidence integration and triangulation for global health research | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Samuel Manda | Organizer: Samuel Manda |
| A1232: B.A. Ejigu, S. Manda | |
| Estimating spatial distribution of HIV prevalence in South Africa from multiple survey data sources | |
| A1558: G. Singini, S. Manda | |
| A Bayesian hierarchical hidden Markov model for infectious diseases time series | |
| A1589: M. Makuta, S. Manda | |
| Assessing methods for detecting outliers in meta-analysis | |
| A1403: S. Manda | |
| Statistics integration of health survey data for estimating disease spatial patterns |
| Session CO241 | Room: S0.03 |
| Topics in multivariate and high-dimensional data | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Ritwik Sadhu | Organizer: Nilanjan Chakraborty |
| A0356: S. Pal, S. Ghosal | |
| Bayesian high-dimensional linear regression with sparse projection-posterior | |
| A0380: N. Deb, A. Kuceyeski, S. Basu | |
| Regularized estimation and inference of sparse spectral precision matrices | |
| A0400: R. Sadhu, N. Chakraborty, T. Banerjee | |
| OT rank tests for heterogeneous non-parametric two-sample testing | |
| A1003: S. Das, D. Das, S. Dutta | |
| On a fast and consistent test for equality of means for high-dimensional data |
| Session CO155 | Room: S0.11 |
| Statistical methods for brain imaging data | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Simon Vandekar | Organizer: Simon Vandekar |
| A0395: R. Pan | |
| Improving statistical power of multi-modal associations via de-variation | |
| A0698: H. Kang, M. Kim, Y. Mei, I. Lyu, A. Zoltowski, C. Fonnesbeck, C. Cascio | |
| Whole brain connectivity estimation by GPU-enhanced Gaussian process | |
| A0938: A. Chen | |
| Nonparametric methods for analysis of brain cortical gradients | |
| A1000: A. Scheffler | |
| A Bayesian latent factor model for curve alignment and covariate-dependent smoothing |
| Session CO333 | Room: S0.12 |
| Methods and models for environmental and ecological data II | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Domenico Vitale | Organizer: Domenico Vitale |
| A0341: N. D Angelo, G. Adelfio | |
| Modelling spatiotemporal point processes for environmental applications | |
| A0430: C. Calculli, A. Pollice, L. Ricciotti | |
| Analyzing summer wildfire patterns in Italian municipalities using satellite data | |
| A0427: J. Rodeschini, A. Fusta Moro, A. Moricoli, A. Fasso | |
| Heteroskedastic hidden dynamic geostatistical models for environmental data |
| Session CO289 | Room: S0.13 |
| Personalized medicine and reinforcement learning (virtual) | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Ruoqing Zhu | Organizer: Ruoqing Zhu |
| A1254: Y. Cui, J. Hannig | |
| Fiducial approaches to censored survival data | |
| A1255: W. Sun | |
| Dual active learning for reinforcement learning from human feedback | |
| A1309: Q. Zheng, T. Poddar, M. Kong | |
| Estimation of average treatment effect for survival outcomes with continuous treatment in observational studies | |
| A1608: C. Ye, L. Zhu, R. Zhu | |
| Consistent order determination of Markov decision process |
| Session CO214 | Room: S-1.01 |
| Statistical modelling of text data | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Bettina Gruen | Organizer: Bettina Gruen, Paul Hofmarcher |
| A0320: J. Vavra, B. Gruen, P. Hofmarcher | |
| Time-varying Poisson factorization with an application to U.S. Senate speeches | |
| A0402: M. Assenmacher | |
| One sample, one label: Learning from labels with different degrees of informativeness | |
| A0475: J. Rieger | |
| Monitoring (social) media narratives combining retrospective few-shot classification with continuous topic modeling | |
| A0818: B. Prostmaier, B. Gruen, P. Hofmarcher | |
| Seeded Poisson factorization: Leveraging domain knowledge to fit topic models |
| Session CO003 | Room: S-1.06 |
| Machine learning and optimization with applications | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Chengchun Shi | Organizer: Chengchun Shi |
| A0213: Y. Wang | |
| Harnessing geometric signatures in causal representation learning | |
| A0460: W. Zhou | |
| Bi-level offline reinforcement learning | |
| A0608: M. Borrotti, D. Ferrari | |
| Local optimization for sequential design of experiments via sparse meta-model | |
| A0949: H. Cai | |
| Doubly robust interval estimation for optimal policy evaluation in online learning |
| A1203: A.F.E. Yode, J.P.N. Tchiekre | |
| Minimax risk with random normalizing factors in the single-index model | |
| A1199: A.F. Yao, V. Monsan, A. Mothe, D.G.-A. Kouadio, C. Aaron | |
| Kernel density estimation for continuous Riemannian stochastic processes | |
| A1205: N. Gbenro, A.K. Diongue, E.H. Deme | |
| Using EVT to test for outliers | |
| A1198: M. Abdillahi Isman, P. Mbaye, S. Khardani, A.F. Yao, W. Nefzi | |
| Kernel regression estimation for stochastic process with values in a Riemannian manifold |
| Session CO272 | Room: S-2.25 |
| Semi-parametric inference via data integration (virtual) | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Abhishek Chakrabortty | Organizer: Abhishek Chakrabortty |
| A1428: Y. Chen | |
| Robust and efficient high-dimensional inference with surrogate outcomes | |
| A0231: S. Li | |
| Efficient federated learning of the average treatment effect | |
| A0261: Z. Zeng, D. Arbour, A. Feller, R. Addanki, R. Rossi, R. Sinha, E. Kennedy | |
| Continuous treatment effects with surrogate outcomes | |
| A0721: Y. Zhang, K. Chen | |
| Enhancing efficiency and robustness in high-dimensional linear regression with additional unlabeled data |
| Session CO146 | Room: Auditorium |
| EcoSta journal | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Cristian Gatu | Organizer: Erricos Kontoghiorghes, Ana Colubi |
| A0305: A. Ghosh, Y.-H. Chen, R. Wu | |
| Heterogeneous Graphon JSQ(d) model | |
| A1160: H. Murakami, H. Yamaguchi | |
| On modified Anderson-Darling statistic for various distributions with unknown parameters | |
| A0358: Z. Cen, C. Lam | |
| Matrix-valued factor model with time-varying main effects | |
| A1425: E. Marceau | |
| Stochastic representation, efficient computation methods, and stochastic ordering in tree-structured Ising models |
| Session CO078 | Room: BH (S) 2.01 |
| ESG and financial-economic sustainability | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Caterina Morelli | Organizer: Caterina Morelli, Paolo Maranzano |
| A0667: P. Galfrascoli, E. Ossola, G. Monti | |
| The greenness of European green bonds in an asset pricing setting | |
| A0897: S. Storani | |
| Analyzing the interplay between ESG dimensions and corporate performance using a multiplex network approach | |
| A0990: C. Sartirana, M.L. Mancusi, B. Baesens | |
| Innovators network and green firms: An analysis on the propensity of becoming a green innovator | |
| A0890: C. Morelli, S. Boccaletti, P. Maranzano, P. Otto | |
| Pattern and dynamics of ESG scores of European firms: Spatiotemporal cluster analysis |
| Session CO073 | Room: BH (S) 2.02 |
| New frontiers in artificial intelligence applications (virtual) | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Emanuela Raffinetti | Organizer: Emanuela Raffinetti |
| A0529: E. Raffinetti, P. Giudici, G. Babaei | |
| Investigating the RGB approach for safe AI | |
| A0252: A. Spelta | |
| The effect of foreign direct investments on CO2 emissions, evidence from Asia | |
| A0219: P. Gloria, M.C. Recchioni, F. Mariani, M. Ciommi | |
| Forecasting composite indicators: The role of environmental variables | |
| A0175: P. Neelakantan | |
| Culture "profiling", AI and AML: Efficacy vs ethics |
| Session CO216 | Room: BH (S) 2.05 |
| Subjective beliefs, surveys, and options data | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Alberto Quaini | Organizer: Alberto Quaini |
| A0646: M. Ibert, M. Dahlquist | |
| Institutions' return expectations across assets and time | |
| A0873: T. Jensen | |
| Subjective risk and return | |
| A0904: S.A. Korsaye | |
| Investor beliefs and trading frictions | |
| A1104: A. Quaini, S.A. Korsaye, G. Freire | |
| The dynamics of subjective risk, risk premia and beliefs |
| Session CO238 | Room: BH (SE) 1.01 |
| Advances in Bayesian variable selection and computing | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Vivekananda Roy | Organizer: Vivekananda Roy |
| A1210: Q. Zhou | |
| Generalized Markov chain importance sampling methods | |
| A1219: G. Zanella, F. Pozza | |
| Zero-order parallel sampling | |
| A1221: S. Livingstone | |
| Preconditioning in Markov chain Monte Carlo | |
| A1324: J. Griffin | |
| Adaptive neighborhood methods for Bayesian variable selection and structure learning |
| Session CO338 | Room: BH (SE) 1.02 |
| Bayesian methods for data with latent structure | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Deborah Kunkel | Organizer: Deborah Kunkel |
| A0266: F. Morgante, P. Carbonetto, G. Wang, Y. Zou, A. Sarkar, M. Stephens | |
| A variational empirical Bayes approach to multivariate multiple regression, with applications to polygenic prediction | |
| A0511: S. Wade, C. Balocchi | |
| Understanding uncertainty in Bayesian clustering | |
| A0958: E. Peterson, L. Waller | |
| Incorporating heterogeneous types of uncertainty in small area estimates from multiple demographic data sources | |
| A1087: M. Griffin | |
| Bayesian generalized linear models for correlated data with fewer latent variables |
| Session CO048 | Room: BH (SE) 1.05 |
| Machine learning for financial data | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Andrii Babii | Organizer: Andrii Babii |
| A0710: A. Babii, E. Ghysels, J. Pan | |
| Tensor PCA for factor models | |
| A0704: B. Chen, Y. Han, Q. Yu | |
| Estimation and inference for CP tensor factor models | |
| A0974: J. Huang, S. Kozak | |
| Data and model uncertainty in the cross-section of equity returns | |
| A0992: M. Pelger, D. Filipovic, Y. Ye | |
| Stripping the discount curve: A robust machine learning approach |
| Session CO329 | Room: BH (SE) 1.06 |
| Recent developments in Bayesian methods | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Hee Cheol Chung | Organizer: Hee Cheol Chung |
| Session CO190 | Room: BH (SE) 2.01 |
| Advances on Bayesian biostatistics and bioinformatics (virtual) | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Marco Ferreira | Organizer: Marco Ferreira |
| A1005: M. Ferreira, J. Williams, S. Xu | |
| Genome-wide iterative fine-mapping for related individuals | |
| A1020: A. Tegge, T. Dolkar, M. Ferreira, H. Shin | |
| Bayesian dynamic clustering factor models: Estimating subgroups and transitions | |
| A0950: S. Xu, M. Ferreira, J. Williams, A. Tegge | |
| Genome-wide iterative fine-mapping for non-Gaussian data | |
| A0976: T. Dolkar, M. Ferreira, A. Tegge, H. Shin | |
| Bayesian dynamic clustering factor models with regressors |
| Session CO163 | Room: BH (SE) 2.10 |
| Nonparametric, semiparametric, and functional data analysis | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Jiaying Weng | Organizer: Jiaying Weng |
| A0493: M. Li, T. Cai, M. Liu | |
| Semi-supervised triply robust inductive transfer learning | |
| A0752: Q. Wang | |
| Dimension reduction through imbalanced learning | |
| A1017: K.-Y. Lee, L. Li | |
| Functional sufficient dimension reduction through average Frechet derivatives | |
| A0931: J. Weng, S.Y. Samadi | |
| Sufficient dimension reduction for high-dimensional nonlinear vector autoregressive models |
| Session CO258 | Room: BH (SE) 2.12 |
| Measurement error and missing data in studies of time and space | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Sarah Lotspeich | Organizer: Sarah Lotspeich |
| A0492: S. Lotspeich | |
| Connecting healthy food proximity and disease: Straight-line vs. map-based distances | |
| A0580: J. Kwon, S. Hepler, D. Kline | |
| A Bayesian hierarchical model to account for temporal misalignment in American community survey explanatory variables | |
| A0367: C. Heffernan | |
| Spatial filtering for unified calibration of air pollution data from multiple low-cost sensor networks | |
| A0360: K. Keller | |
| Inferential challenges with spatial data in air pollution epidemiology |
| Session CO094 | Room: Safra Lec. Theatre |
| Functional and distributional data analysis | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Sonja Greven | Organizer: Sonja Greven |
| A0351: J. Goldsmith, A. Mendez-Civieta, Y. Wei, K. Diaz | |
| Functional quantile principal component analysis | |
| A0385: C. Thomas-Agnan | |
| A Bayes space point of view on climate warming | |
| A1149: B. Caffo, B. Smith | |
| Can AI learn distributional regression | |
| A0674: M. Matabuena, P. Mozharovskyi, O.H. Madrid Padilla, J.-P. Onnela, R. Ghosal | |
| Conformal uncertainty quantification using kernel depth measures in separable Hilbert spaces |
| Session CO031 | Room: K2.31 (Nash Lec. Theatre) |
| Flexible learning in complex data environments | Monday 16.12.2024 14:40 - 16:20 |
| Chair: David van Dyk | Organizer: Christopher Hans |
| A1454: R. Zimmerman, D. van Dyk, V. Kashyap, A. Siemiginowska | |
| Separating states in astronomical sources using hidden Markov models | |
| A1583: A. Volfovsky | |
| Experimental design for modern settings: Stories about text | |
| A1436: C. Hans, N. Liu | |
| Model comparison for Bayesian lasso-like regression | |
| A1505: C. Doss | |
| Doubly robust pivotal pointwise confidence intervals for a monotonic continuous treatment effect curve |
| Session CO362 | Room: BH (S) 1.01 Lec. Theathre 1 |
| Analysis of non-stationary time series | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Yunyi Zhang | Organizer: Yunyi Zhang |
| A0483: I. Chronopoulos, A. Chrysikou, G. Kapetanios | |
| High-dimensional generalized penalized least squares | |
| A0484: A. Chrysikou, G. Kapetanios | |
| Heterogeneous grouping structures in panel data | |
| A0507: A. Yuan, W. Shou | |
| A nonparametric test for correlation between nonstationary time series: Addressing challenges with limited replicates | |
| A0591: Y. Zhang, E. Paparoditis, D. Politis | |
| Bootstrap-assisted inference for weakly stationary time series |
| Session CC466 | Room: S-1.04 |
| Stochastic processes | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Stefan Wrzaczek | Organizer: CFE-CMStatistics |
| A1090: S. Suzuki | |
| Adaptive Bayes estimator for stochastic differential equation with jumps under small noise asymptotics | |
| A1246: S. Kusano, M. Uchida | |
| Quasi-Akaike information criterion of SEM for diffusion processes | |
| A1405: G. Amici, L. Ballotta, P. Semeraro | |
| Multivariate additive subordination with applications in finance | |
| A1445: A. Srakar | |
| Linear regression with Bouchaud's stochastic aging |
| Session CC420 | Room: BH (S) 2.03 |
| Financial econometrics II | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Weining Wang | Organizer: CFE-CMStatistics |
| A1236: T. Kobayashi | |
| Decomposing the term structure of credit spreads and predicting the macroeconomy in Japan | |
| A1258: K. Bien-Barkowska, A. Kliber, R. Herrera | |
| Analyzing the EPU effect on the risk of extreme events in the oil market: A MIDAS touch to dynamic POT models | |
| A1674: X. Zou, A. Lucas, Y. Lin | |
| Closing the gap between state-space and score-driven models | |
| A1398: O.K. Ayensu, Y. Feng, D. Schulz | |
| Recent extensions of short- and long memory volatility and duration models implemented with R |
| Session CC447 | Room: BH (SE) 2.05 |
| Applied econometrics | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Nicola Loperfido | Organizer: CFE-CMStatistics |
| A1449: S. Roszkowska, A. Majchrowska | |
| Minimum wage and health status in Europe | |
| A1307: U. Aiounou | |
| Treatment effect estimation in high-dimension: An inference-based approach | |
| A0255: M.R. Nieto Delfin, O.A. Saucedo Delgado, J.S. Valades-Garcia | |
| Comparative analysis of the efficiency of health systems in OECD countries (2000-2020) | |
| A1374: I. van de Werve, S.J. Koopman | |
| Dynamic panel data models for the potential European crime drop |
| Session CC498 | Room: BH (SE) 2.09 |
| Forecasting II | Monday 16.12.2024 14:40 - 16:20 |
| Chair: Antoine Djogbenou | Organizer: CFE-CMStatistics |
| A1546: R. McGee, V. Poti, T. Post | |
| Option-implied physical distributions | |
| A1378: A. Bucci, M. Palma, C. Zhang | |
| Geometric deep learning for realized covariance matrix forecasting | |
| A1613: Y. Ulu | |
| Forecasting dynamic correlation via a hybrid deep learning: Multivariate DCC GARCH model |
| Parallel session Q: CFECMStatistics2024 | Monday 16.12.2024 | 16:50 - 18:30 |
| Session CO037 | Room: K0.16 |
| Statistical analysis of network and other complex data | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Yunpeng Zhao | Organizer: Yunpeng Zhao |
| Session CO019 | Room: K0.18 |
| Methodology and practice for data originating from randomized trials | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Andrew Spieker | Organizer: Andrew Spieker |
| A1231: B. Blette | |
| Exploring the nature of individualized treatment effects using a large crossover trial | |
| A1253: J. Chipman, O. Sverdlov, D. Uschner | |
| Experimenting with finite to infinite populations | |
| A1297: A. Hackstadt, C. Lwin, R. Greevy, K. Snyder, C. Roumie | |
| A Bayesian approach to studying major adverse cardiovascular events: Leveraging information from clinical trials | |
| A1469: A. Spieker | |
| Causal mediation analysis of engagement for randomized trials involving mobile health interventions |
| Session CO166 | Room: K0.19 |
| Statistical innovations in clinical trial design and analysis | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Chenguang Wang | Organizer: Chenguang Wang |
| Session CO242 | Room: K0.20 |
| Statistical Inference with graphs | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Robert Lunde | Organizer: Nilanjan Chakraborty |
| A0447: R. Chen, J. Cai, H. Shen, D. Yang, L. Zhao, W. Zhu | |
| Network regression and supervised centrality estimation | |
| A0824: W. Li, R. Lunde, N. Chakraborty | |
| Assumption-lean inference for the network-linked data | |
| A0844: R. Lunde, L. Levina, J. Zhu | |
| Conformal prediction for Dyadic regression | |
| A1050: A. Green | |
| Two-sample testing with a graph-based total variation integral probability metric |
| Session CO269 | Room: K0.50 |
| Recent innovations in two-phase study design and analysis | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Qihuang Zhang | Organizer: Fangya Mao |
| Session CO277 | Room: K2.40 |
| Spatial data science | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Philipp Otto | Organizer: Philipp Otto |
| A0174: P. Craigmile, P. Guttorp | |
| Spatiotemporal comparison of sea surface to air temperatures in the tropical Pacific | |
| A0188: A. Groll | |
| LASSO-type penalization in the framework of generalized additive models for location, scale and shape (GAMLSS) | |
| A0216: T. Senga Kiesse, N. Ouachene, M. Corson, C. Czado | |
| Copula-based regressions for assessing trade-offs between milk production and greenhouse-gas emissions of French farms | |
| A0227: A. Rakitzis, M. Anastasopoulou | |
| Control charts for monitoring a BINARCH(1) process: Comparisons and applications |
| Session CO402 | Room: K2.41 |
| ROC curves/evaluation of biomarkers | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Vanda Inacio | Organizer: Ainesh Sewak, Vanda Inacio |
| A0473: M.X. Rodriguez Alvarez | |
| A unified framework for ROC curve inference with and without covariates | |
| A0354: P. Martinez-Camblor, J.-C. Pardo-Fernandez | |
| Semiparametric estimator for the covariate-specific ROC curve | |
| A0939: A. Sewak, V. Inacio, T. Hothorn | |
| Evaluating prognostic biomarkers for censored survival data with covariate adjustment | |
| A0923: L. Bantis, B. Brewer | |
| Biomarker cutoff estimation and their confidence intervals under ternary umbrella and tree stochastic ordering settings |
| Session CO164 | Room: S0.03 |
| Handling complex data and complexity | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Jacopo Di Iorio | Organizer: Jacopo Di Iorio |
| A1039: B. Roycraft | |
| Feature generating models: Inference in purely high dimensions | |
| A1377: E. Arnone, M. Tomasetto, L. Sangalli | |
| Modeling spatial anisotropy and non-stationarity in semiparametric regression with differential penalization | |
| A1591: Y. Fan | |
| Forecasting curves portions using motif discovery inspired method | |
| A1610: A. Kenney, T. Tang, M. Huang | |
| Distilling causal effects: Stable subgroup estimation via distillation trees in causal inference |
| Session CO067 | Room: S0.11 |
| Novel statistical methods and analyses for neuroimaging and biomedical data | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Cai Li | Organizer: Cai Li |
| Session CO265 | Room: S0.12 |
| Methods for multiparameter evidence synthesis and spatial omics data | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Zelalem Negeri | Organizer: Zelalem Negeri |
| A0284: L. Lin | |
| Advancing trial sequential analyses for living systematic reviews | |
| A0760: J. Beyene | |
| Comparison of dose-response meta-analytic models using empirical and simulation studies | |
| A0682: A. Wigle, A. Beliveau, G. Salanti, G. Rucker, G. Schwarzer, D. Mavridis, A. Nikolakopoulou | |
| Precision of treatment hierarchy: A metric for quantifying certainty in treatment hierarchies from network meta-analysis | |
| A0737: H. Jones, M. Hickman, A. Markoulidakis | |
| Multi-parameter estimation of prevalence (MPEP) models to estimate the prevalence of opioid dependence | |
| A0726: P. Jeganathan, R. Senanayake | |
| Enhance constraint spatial partitioning for spatial omics data |
| Session CO029 | Room: S0.13 |
| Recent advances in complex analysis of genomics data | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Li-Xuan Qin | Organizer: Li-Xuan Qin |
| Session CO388 | Room: S-1.04 |
| New investigators in computational statistics (virtual) | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Rob Deardon | Organizer: Laura Cowen |
| A0267: L. Hagar, N. Stevens | |
| Design of posterior analyses with sampling distribution segments | |
| A0299: G. Dong, L. Cowen | |
| An integrated population model to estimate the population size of persons contending with homelessness | |
| A0506: M. Parker, L. Cowen, J. Cao, L. Elliott | |
| Computational efficiency and precision for replicated-count and batch-marked hidden population models | |
| A0954: K. Olobatuyi, P. Brown, L. Cowen | |
| Multievent dynamic capture-recapture model: Estimating undetected COVID-19 cases in British Columbia, Canada |
| Session CO400 | Room: S-1.06 |
| Geometric data analysis for complex data structures | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Carlos Soto | Organizer: Carlos Soto |
| A0989: C. Soto | |
| Functional Gaussian differential privacy for private human faces | |
| A0325: Y.J. Choi, S. Kurtek, K. Bharath | |
| Analyzing spatial dependence in functional data and shapes of 2D curves | |
| A0987: S. Tymochko | |
| A topological approach to analyzing access to resources with heterogeneous quality | |
| A1027: S. Mohammed | |
| Quantifying imaging heterogeneity via density functions with applications in brain and pancreatic cancer imaging |
| Session CO021 | Room: S-1.27 |
| Recent advances of machine learning in interdisciplinary problems | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Tianxi Li | Organizer: Tianxi Li |
| A1029: D. Kessler, E. Ancell, D. Witten | |
| Selective inference after community detection on a single network | |
| A1566: H. Xu | |
| Algorithms and incentives in machine learning | |
| A1596: Y. Yan | |
| Isotonic mechanism for exponential family estimation in machine learning peer review | |
| A1685: A. Yang | |
| Flexible regularized estimating equations: Some new perspectives |
| Session CO147 | Room: S-2.25 |
| Design and analysis of studies in kidney disease (virtual) | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Jarcy Zee | Organizer: Jarcy Zee |
| A0650: M. Montez Rath, I.-C. Thomas, V. Charu, M. Odden, C. Dacey, S. Arya, E. Fung, A. OHare, S. Wong, M. Kurella Tamura | |
| Target trial emulation to assess the effect of starting dialysis versus continuing medical management | |
| A0680: A. Smith, M. Helmuth, L. Mariani | |
| Identifying new clinical trial surrogate endpoints in rare diseases: The PARASOL approach | |
| A1114: J. Zee, Q. Liu, J. Rubin, F. Fan, L. Barisoni, A. Janowczyk | |
| Feature selection and outcome prediction using kidney pathomic data | |
| A0959: V. Charu, T. Pan, L. Tian | |
| Heterogeneous treatment effect estimation for longitudinal outcomes |
| Session CO100 | Room: Auditorium |
| Machine learning in asset pricing | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Markus Pelger | Organizer: Markus Pelger |
| A0558: H. Ma | |
| Conditional latent factor models via model-based neural networks | |
| A0652: P. Schneider, P. Whelan, M. Van Uffelen | |
| On the consumption wealth return | |
| A1033: P. Zaffaroni | |
| Portfolio choice with unsystematic risk | |
| A1217: D. Bianchi, P. Moravis Venturi | |
| Firm characteristics and the cross-section of stock returns: A tale of two tails |
| Session CO407 | Room: BH (S) 2.02 |
| Climate risk uncertainty | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Maria Elena Bontempi | Organizer: Maria Elena Bontempi |
| A0200: A. Bhargava | |
| An econometric analysis of agricultural production, groundwater depletion and glacier thicknesses | |
| A0321: A. Luati, L. Catania, E. DInnocenzo | |
| Unobserved component models, approximate filters and dynamic adaptive mixture models | |
| A0669: M. Abolhassani, J. Ditzen | |
| Spillover effect of private equity investment: Evidence from Italy | |
| A0743: M.E. Bontempi, G. Angelini, P. Neri, L. De Angelis, M.M. Sorge | |
| A new index of climate concern and identification of shocks: A proxy-SVAR approach |
| A0745: R. Kunst, M. Ertl, A. Wende | |
| On the influence of the choice of seasonal adjustment method on forecasting national accounts aggregates across the EU | |
| A0854: I. Fortin, J. Hlouskova | |
| Commodity price uncertainty and macroeconomic dynamics | |
| A0209: M. Jahan-Parvar, J. Rahman, Y. Kitsul, B.A. Wilson | |
| Foreign economic policy uncertainty and the U.S. equity returns | |
| A0229: M. Beyhaghi | |
| The information advantage of banks: Evidence from their private credit assessments |
| Session CO199 | Room: BH (S) 2.05 |
| Flexible estimations | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Jing Zhou | Organizer: Jing Zhou, Eugen Pircalabelu |
| A0829: X. Yu | |
| Factor augmented tensor-on-tensor neural networks | |
| A1191: C. Li | |
| Learning a directed acyclic graph with heteroscedastic errors | |
| A1229: L. Leonard, E. Pircalabelu, R. von Sachs | |
| High-dimensional regression: Model averaging and inference | |
| A1515: O. Evkaya | |
| Dependence of drought characteristics: Parametric and non-parametric copula approach |
| Session CO120 | Room: BH (SE) 1.01 |
| Bayesian statistical learning in practice | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Alejandro Murua | Organizer: Alejandro Murua |
| A0889: G. Kon Kam King | |
| High-dimensional variable selection in non-linear mixed-effects models | |
| A0751: T. Chekouo | |
| Incorporating gene ontology and disease ontology into Bayesian feature selection for cancer subtypes | |
| A0352: M. Bedard | |
| Performance of the annealed MALA in transience and in stationarity | |
| A1350: A. Barrientos, J. Awan, N. Ju | |
| Statistical inference for privatized data with unknown sample size |
| Session CO181 | Room: BH (SE) 1.02 |
| Advances in Bayesian smoothing and recursive partitioning | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Sameer Deshpande | Organizer: Sameer Deshpande |
| A0298: S. Deshpande | |
| Scalable targeted smoothing in high-dimensions with BART | |
| A0326: H. Luo | |
| Geometric shapes of the tree-induced partition | |
| A0416: P. Ma | |
| Residual tree Gaussian processes | |
| A0948: P. Wiemann, M. Katzfuss | |
| Efficient non-Gaussian variational inference for continuous functions using sparse autoregressive normalizing flows |
| Session CO186 | Room: BH (SE) 1.06 |
| Distribution-lean statistical inference | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Arun Kuchibhotla | Organizer: Arun Kuchibhotla |
| A1502: M. Paul, A. Kuchibhotla | |
| Inference for median and a generalization of HulC | |
| A1503: W. Chang | |
| Inference for projection parameters in linear regression: beyond $d = o(n^{1/2})$ | |
| A1509: K. Takatsu | |
| Dimension-agnostic inference for M-estimation | |
| A1514: S. Sarkar, A. Kuchibhotla | |
| Unified framework for inference using confidence sets for the CDF |
| Session CO092 | Room: BH (SE) 2.05 |
| Statistical inference, modeling, and optimal design | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Subir Ghosh | Organizer: Subir Ghosh |
| A1215: J. Ghosh, A. Tan, L. Luo | |
| Online Bayesian model averaging for streaming data | |
| A1252: A. Guthrie, C. Franck | |
| Responsibly emboldening predictions via boldness-recalibration | |
| A1385: K.O. Ekvall | |
| Confidence regions when the parameter is near the boundary | |
| A1212: A. Mahmoudi, S. Mandal | |
| Optimal design construction: A comparative study of various methods |
| Session CO020 | Room: BH (SE) 2.09 |
| Individual heterogeneity for macroeconomic fluctuations | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Michele Lenza | Organizer: Michele Lenza |
| A1147: S. Ettmeier, F. Schorfheide | |
| Measuring the effects of aggregate shocks on unit-level outcomes and their distribution | |
| A1186: M. Elfsbacka Schmoller | |
| Long-run neutrality meets reality: Innovation and monetary policy | |
| A1144: M. Lenza, E. Savoia | |
| On the need of firm data to understand macroeconomic dynamics | |
| A1609: L. Pollio, S. Pesce, M. Errico | |
| Firms heterogeneity and aggregate fluctuations: What can we learn from machine learning |
| Session CO354 | Room: BH (SE) 2.10 |
| Spatial statistics and its applications | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Rajarshi Guhaniyogi | Organizer: Rishideep Roy |
| A1651: S. Deb, A. Roy, S. Basu Sarbadhikary | |
| Structural breaks in the spatial network of real estate dynamics: A study of UK property transactions | |
| A1569: S. Dutta, D. Mondal | |
| Matrix-free conditional simulation of Gaussian random fields | |
| A1598: D. Prata Gomes, C. Cordeiro, M. Neves | |
| Statistical analysis of extreme geostatistical data: Challenges and advances | |
| A0197: S. Liverani | |
| Bayesian modelling for spatially misaligned health areal data |
| Session CO309 | Room: BH (SE) 2.12 |
| Statistical methods for analyzing high-dimensional cancer datasets | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Subharup Guha | Organizer: Subharup Guha |
| A0369: W. Li, C. Chang, S. Kundu, Q. Long | |
| Accounting for network noise in graph-guided Bayesian modeling of high-dimensional-omics data | |
| A0375: J. Satagopan, S. Sarkar | |
| Harnessing sociocultural similarities between diverse populations to identify determinants of cancer screening use | |
| A0378: S. Salerno, Y. Li | |
| A pseudo-value approach to causal deep learning of semi-competing risks | |
| A0875: D. Dutta | |
| Identifying genes associated with disease outcomes using joint sparse canonical correlation analysis |
| Session CO034 | Room: Safra Lec. Theatre |
| Statistical learning with complex functional data | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Haolun Shi | Organizer: Haolun Shi |
| A0414: H. Shi | |
| Identification of regions of interest in neuroimaging data based on semiparametric transformation models | |
| A1584: H. Wang | |
| Representation learning of dynamic networks | |
| A1592: Z. Zhou | |
| Enhancing the risk prediction for underrepresented groups | |
| A1628: T. Guan, S. Jia, H. Shi | |
| Function-on-function combined regression models |
| Session CO143 | Room: K2.31 (Nash Lec. Theatre) |
| New advances in causal inference | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Liqun Diao | Organizer: Liqun Diao |
| A0398: E. Moodie, M. Dolmatov, D. Bandyopadhyay | |
| Optimal treatment allocation in the presence of competing risks and clustering | |
| A0836: L. Wen | |
| Identification and estimation of the average causal effects under dietary substitution strategies | |
| A0908: A. Ertefaie, C. Pham, B. Baer | |
| Nonparametric assessment of regimen response curve estimators | |
| A0996: M. Wallace | |
| All else being equal: Implications of measurement error for precision medicine and health equity |
| Session CO252 | Room: BH (S) 1.01 Lec. Theathre 1 |
| Modern change-point analysis | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Hao Chen | Organizer: Hao Chen |
| A0241: Y. Xie, S. Wei | |
| Online kernel CUSUM for change-point detection | |
| A0246: X. Zhang, G. Li, X. Li | |
| Segmenting watermarked texts from language models | |
| A0730: D. Zhou, H. Chen | |
| Asymptotic distribution-free change-point detection for modern data based on a new ranking scheme | |
| A0816: L. Lai | |
| Two-stage sequential change diagnosis problems |
| Session CC479 | Room: S-1.01 |
| Software | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Masayuki Hirukawa | Organizer: CFE-CMStatistics |
| A1595: B. Hansen, J. Errickson, J. Wasserman, A. Sales | |
| Safety first: Design-informed inference for treatment effects via the propertee package for R | |
| A1587: M.H. Goncalves, M.S. Cabral | |
| Analyze of count longitudinal data with random effects using R packages, cold, lme4 and glmmML | |
| A1677: M. Oviedo de la Fuente, M. Febrero-Bande | |
| Functional data clustering in R | |
| A1396: L. McMillan, D. Fernandez, S. Pledger, R. Arnold, I. Liu, M. Efford | |
| "clustglm" and "clustord": R packages for clustering with covariates for binary, count, and ordinal data |
| Session CC430 | Room: BH (S) 2.01 |
| Time series econometrics | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Weining Wang | Organizer: CFE-CMStatistics |
| A0392: W. LI, B. Nguyen | |
| Going upstream: Responses of drilling activity to global oil markets | |
| A1238: C. Amoroso | |
| New proposal for seasonal adjustment of long time series | |
| A1554: S. Rauhala | |
| Forecasting with dynamic factor models estimated by partial least squares | |
| A1681: T. Wada, A. Noda | |
| Forecasting cryptocurrency returns with a sparse dynamic factor model |
| Session CC483 | Room: BH (SE) 1.05 |
| Empirical finance | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Nicola Loperfido | Organizer: CFE-CMStatistics |
| A1046: M. Karoglou, E. Platanakis, D. Stafylas | |
| Probability forecasts: A simple albeit powerful predictor for hedge fund returns | |
| A1537: P. Uberti, M.-L. Torrente | |
| An empirical comparison between investing strategies: Maximum diversification versus minimum risk | |
| A1635: G. Power, M.-H. Gagnon, C. Aka | |
| Volatility transmission between commodity option and futures markets | |
| A1494: A. Iona, L. Leonida, D.Z. Assefa | |
| Political competition, democracy and financial development: Cross-country evidence |
| Session CC495 | Room: BH (SE) 2.01 |
| Data analysis and empirical studies | Monday 16.12.2024 16:50 - 18:30 |
| Chair: Joshua Cape | Organizer: CFE-CMStatistics |
| A1419: M. Bee, F. Santi | |
| A comparison of numerical maximum likelihood and noise-contrastive estimation for unnormalized statistical models | |
| A1650: C.D. Kim | |
| Quantifying the intrinsic data quality of process data | |
| A1600: S. Faria, A. Moreira | |
| Comparisons of variable selection methods in mixtures of linear regression models | |
| A1560: E. Erturk, J. Unwin Teji, P. Raynham | |
| Odds ratio for assessing the risk of crime associated with darkness |