KEYNOTE TALKS (UTC+1)
| Keynote talk I | Tuesday 27.8.2024 | 09:40 - 10:30 | Room: Auditorioum 4 |
| On regression with convex classes | |||
| Speaker: S. van de Geer | Chair: Peter Winker | ||
| Keynote talk II | Thursday 29.8.2024 | 17:40 - 18:30 | Room: Auditorioum 4 |
| Quantifying uncertainty with Bayesian deep learning | |||
| Speaker: N. Klein | Chair: Sonja Greven | ||
| Keynote talk III | Friday 30.8.2024 | 12:10 - 13:00 | Room: Auditorioum 4 |
| An extended latent factor framework for ill-posed generalised linear regression | |||
| Speaker: T. Krivobokova Co-authors: G. Finocchio | Chair: Maria Brigida Ferraro | ||
PARALLEL SESSIONS (UTC+1)
| Parallel session B: COMPSTAT2024 | Tuesday 27.8.2024 | 11:00 - 12:30 |
| Session CI006 (Special Invited Session) | Room: 45 |
| Recent advances in optimal design of experiments | Tuesday 27.8.2024 11:00 - 12:30 |
| Chair: Stefanie Biedermann | Organizer: Stefanie Biedermann |
| A0292: L. Filova, R. Harman, P. Somogyi | |
| Computing constrained optimal designs with applications to dose-finding | |
| A0198: M. Prus | |
| Optimizing the allocation of trials to sub-regions in multi-environment crop variety testing for correlated genotypes | |
| A0375: K. Schorning | |
| Optimal designs for state estimation in networks |
| Session CO082 | Room: 44 |
| Text mining | Tuesday 27.8.2024 11:00 - 12:30 |
| Chair: Philipp Adaemmer | Organizer: Philipp Adaemmer |
| A0162: M. Weinig, U. Fritsche | |
| Going viral: Inflation narratives and the macroeconomy | |
| A0189: J. Rieger | |
| PETapter: A masked-language-modeling classification head for modular fine-tuning of (large) language models | |
| A0190: P. Labonne, L.A. Thorsrud | |
| Risky news and credit market sentiment | |
| A0334: E. Toenjes, C. Funk, C. Haas | |
| Exploring the predictive capacity of ESG sentiment on official ratings: A few-shot learning perspective |
| Session CO124 | Room: 052 |
| Nonparametric inference and inverse problems | Tuesday 27.8.2024 11:00 - 12:30 |
| Chair: Fabian Dunker | Organizer: Fabian Dunker |
| A0400: A. Vanhems | |
| A mollification approach to stabilize econometrics models: Application to deconvolution and random coefficients models | |
| A0345: A. Meister | |
| Nonparametric estimation under Gaussian measurement error with conditionally heteroscedastic variances | |
| A0268: K. Proksch, C. Weitkamp, T. Staudt, C. Zimmer, B. Lelandais | |
| From small scales to large scales: Distance-to-measure density based geometric analysis of complex data | |
| A0297: C. Breunig | |
| Doubly robust Bayesian Difference-in-Differences estimators |
| Session CC051 | Room: 43 |
| Functional data analysis | Tuesday 27.8.2024 11:00 - 12:30 |
| Chair: Sonja Greven | Organizer: COMPSTAT |
| A0378: I. Pavlu, A. Stoecker, A. Czolkova, K. Hron, S. Greven | |
| Density-on-scalar regression for bivariate distributions in Bayes spaces | |
| A0450: S. Skorna, J. Machalova, K. Hron | |
| Dealing with count zeros in preprocessing of probability density functions | |
| A0480: L. Sort, L. Le Brusquet, A. Tenenhaus | |
| Functional PARAFAC with probabilistic modeling | |
| A0484: Q.E. Seifert, E. Bergherr, T. Hepp | |
| Function-on-scalar regression via first-order gradient-based optimization |
| Session CC133 | Room: 050 |
| Financial time series | Tuesday 27.8.2024 11:00 - 12:30 |
| Chair: Alessandra Amendola | Organizer: COMPSTAT |
| A0441: D. Rosadi, P. Filzmoser, L. Gubu, L. Vemmie Nastiti | |
| Portfolio optimization using hybrid robust time series clustering and robust mean-variance portfolio selection | |
| A0479: P. Hubner, J. Hambuckers | |
| Which early warning signals predict high-frequency extreme price movements? | |
| A0391: R. Yabe | |
| Wald-type test for conditional moving average unit root | |
| A0188: E. Zarova | |
| Combining caterpillar-SSA methods and mixed frequency data regression for inflation forecasting |
| Session CC047 | Room: 051 |
| Spatial statistics | Tuesday 27.8.2024 11:00 - 12:30 |
| Chair: Mattias Villani | Organizer: COMPSTAT |
| A0427: P. Kuendig, F. Sigrist | |
| Iterative methods for Vecchia-Laplace approximations for latent Gaussian process models | |
| A0485: E. Siviero, G. Staerman, S. Clemencon, T. Moreau | |
| Flexible inference for spatiotemporal Hawkes processes with general parametric kernels | |
| A0394: C. Buelte, L. Leimenstoll, M. Schienle | |
| Estimation of spatiotemporal extremes via generative neural networks | |
| A0417: O. Oyebamiji | |
| Deep parametric predictive Gaussian processes for uncertainty estimation |
| Parallel session C: COMPSTAT2024 | Tuesday 27.8.2024 | 14:00 - 15:30 |
| A0347: E. Lazar, S. Wang, J. Pan | |
| Measuring climate-related and environmental risks for equities | |
| A0315: M. Podolskij | |
| Recent advances in high dimensional estimation of diffusion models | |
| A0167: M. Caporin, G. Bonaccolto, J. Shahzad | |
| (Quantile) Spillover indexes: Simulation-based evidence, confidence intervals and a decomposition |
| Session CO084 | Room: 43 |
| Advances in functional and complex data analysis | Tuesday 27.8.2024 14:00 - 15:30 |
| Chair: Bo Wang | Organizer: Bo Wang |
| A0283: P. Reiss, B. Paul, N. Foa, D. Arbiv | |
| Interval estimation for continuous-time correlation | |
| A0287: M. Gupta | |
| Bayesian hierarchical latent variable-based modelling for large and complex genomic datasets | |
| A0385: P. Wongsa-art | |
| Cross-national comparisons of Covid-19 lockdown effectiveness: The spatial functional data analysis approach | |
| A0243: B. Wang | |
| Clusterwise nonlinear regression with Gaussian processes methods |
| Session CO119 | Room: 45 |
| Spatio-temporal and network analysis | Tuesday 27.8.2024 14:00 - 15:30 |
| Chair: Carsten Jentsch | Organizer: Carsten Jentsch |
| A0202: Y. Chen | |
| Matrix autoregressive model with vector time series covariates for spatio-temporal data | |
| A0227: P. Otto | |
| A multivariate spatial and spatiotemporal ARCH Model | |
| A0303: J. Flossdorf, D. Dzikowski, C. Jentsch | |
| Autoregressive dynamic network modelling with serial and cross-sectional dependence | |
| A0348: A. Kreiss, E. Mammen, W. Polonik | |
| Testing for global covariate effects in dynamic interaction event networks |
| Session CC138 | Room: 050 |
| Change point analysis | Tuesday 27.8.2024 14:00 - 15:30 |
| Chair: Roland Fried | Organizer: COMPSTAT |
| A0222: L. Zhang, R. Drikvandi | |
| A non-parametric method for high dimensional change point analysis | |
| A0369: S. Goerz, R. Fried | |
| Methods for structural change detection in the trend function of random fields | |
| A0406: Z. Yang, P. Fearnhead, I. Eckley | |
| A fast Bayesian online changepoint detection algorithm | |
| A0490: S. Deb | |
| Nonparametric method of changepoint detection in time series data |
| Session CC018 | Room: 051 |
| Bayesian statistics | Tuesday 27.8.2024 14:00 - 15:30 |
| Chair: Mattias Villani | Organizer: COMPSTAT |
| A0184: F. Frommlet, A. Hubin, G.O. Storvik, J. Lachman | |
| FBMS: An R package for Flexible Bayesian Model Selection | |
| A0445: G. Li, R. Leon-Gonzalez | |
| Nuisance parameters, modified profile likelihood and Jacobian prior | |
| A0456: D. Ghani, N. Heard, F. Sanna Passino | |
| Approximate learning of parsimonious Bayesian context trees | |
| A0469: T. Iesmantas, R. Alzbutas | |
| Bayesian learning lithium-ion open circuit voltage curve via state-space model |
| Session CC026 | Room: 052 |
| Applied statistics and data analysis | Tuesday 27.8.2024 14:00 - 15:30 |
| Chair: Stefanie Biedermann | Organizer: COMPSTAT |
| A0419: S. Potts, K. Kurz, A. Rappl, E. Bergherr | |
| Joint models for longitudinal and time-to-event data in the social sciences | |
| A0461: L. An, B. De Baets, S. Luca | |
| Group anomaly detection for optimizing urban planning of rental bike services | |
| A0181: T. Fung, J. Wang | |
| Coherent forecast and criminal justice program evaluation in hierarchical time series | |
| A0429: L. Knieper, T. Kneib, E. Bergherr | |
| Spatial confounding in gradient boosting |
| Parallel session D: COMPSTAT2024 | Tuesday 27.8.2024 | 16:00 - 18:00 |
| Session CO110 | Room: 43 |
| IASC Journal of Data Science, Statistics, and Visualisation (JDSSV) session | Tuesday 27.8.2024 16:00 - 18:00 |
| Chair: Stefan Van Aelst | Organizer: Patrick Groenen, Stefan Van Aelst |
| A0232: M. Hirari, S. Van Aelst, M. Hubert, F. Centofanti | |
| Robust multiway PCA for casewise and cellwise outliers | |
| A0248: A. Maharaj, P. Brito, P. Teles | |
| Comparison of interval time series | |
| A0309: I. Kalogridis, S. Nagy | |
| Robust functional regression with discretely sampled predictors | |
| A0324: S. Leyder, J. Raymaekers, P. Rousseeuw | |
| Is distance correlation robust? | |
| A0337: S. Van Aelst, T. Verdonck, B. Yang | |
| Interpretable cost-sensitive ensembling |
| Session CO086 | Room: 44 |
| Clustering of complex data structures (HiTEc) | Tuesday 27.8.2024 16:00 - 18:00 |
| Chair: Maria Brigida Ferraro | Organizer: Maria Brigida Ferraro |
| A0156: A. Lopez Oriona | |
| Fuzzy clustering of circular time series based on a new dependence measure with applications to wind data | |
| A0312: M. Ranalli, F. Martella | |
| Biclustering of ordinal data through a composite likelihood approach | |
| A0351: C. Rampichini | |
| Two-step clustering: A new method in the sequential deep clustering approach | |
| A0380: P. McNicholas | |
| Clustering three-way data | |
| A0381: M. van de Velden, C. Cavicchia, A. Iodice D Enza, A. Markos | |
| Unbiased mixed variables distance |
| Session CO088 | Room: 45 |
| Reliable prediction models for challenging data | Tuesday 27.8.2024 16:00 - 18:00 |
| Chair: Garth Tarr | Organizer: Garth Tarr |
| A0210: F. Feser, M. Evangelou | |
| Sparse-group SLOPE: Adaptive bi-level selection with FDR-control | |
| A0212: S. Muller, B. Liquet, S. Moka, H. Zhu | |
| Subset selection via continuous optimization | |
| A0221: M. Demosthenous, C. Gatu, E. Kontoghiorghes | |
| Computational strategies for regression model selection in the high-dimensional case | |
| A0273: I. Joudah, S. Muller, H. Zhu | |
| Efficient stability screening for ultra-high dimensional data | |
| A0373: Z. Jiao, Y. Lee | |
| Assessment of case influence in the Lasso with a case-weight adjusted solution path |
| Session CO118 | Room: 052 |
| Statistical methods for energy and transport data | Tuesday 27.8.2024 16:00 - 18:00 |
| Chair: Roland Fried | Organizer: Roland Fried |
| A0229: D. Witthaut | |
| Statistics of the power grid frequency | |
| A0343: A. Arsova, S. Pappert | |
| Probabilistic forecasting and reconciliation of wind turbine power | |
| A0364: N. Ludwig | |
| Probabilistic forecasting of energy time series with diffusion models | |
| A0319: J. Gonzalez-Ordiano, M. Santos-Moreno, K. Obermeier-Velazquez, L. Cortes-Munoz, J. Asse-Amiga, L. Corral-Corona | |
| Statistical methods for power demand and consumption time series at household level in Mexico | |
| A0470: K. Lubashevsky, I. Okhrin, S. Huber, S. Lissner | |
| Analyzing the route choice of cyclists using machine learning models |
| Session CC055 | Room: 050 |
| High-dimensional statistics | Tuesday 27.8.2024 16:00 - 18:00 |
| Chair: Stefan Sperlich | Organizer: COMPSTAT |
| A0241: S. Doehler | |
| A unified class of null proportion estimators with plug-in FDRcontrol | |
| A0478: I. Ullah, A. Welsh | |
| Noise and overfitting: A new perspective on the predictive performance of a linear model | |
| A0435: H. Kobayashi, M. Okabe, H. Yadohisa | |
| Graph-linked unified embedding considering label information | |
| A0502: N. Hernandez, T. Fearn, Y. Choi | |
| Optimizing interval PLS via GP regression | |
| A0186: T.Q. Asenso, M. Zucknick | |
| Accounting for population heterogeneity by modeling interactions with the pliable lasso |
| Session CC135 | Room: 051 |
| Machine learning for applications | Tuesday 27.8.2024 16:00 - 18:00 |
| Chair: Florian Frommlet | Organizer: COMPSTAT |
| A0191: O. Alpay | |
| Impact of rarity level and resampling techniques on machine learning classification performance | |
| A0194: I. Aydin, O. Alpay | |
| On some mathematical foundations of machine learning algorithm and an application | |
| A0454: M.D. Ganggayah, E. Barrios, H. Muniandy | |
| Correlates of suicide ideation among young adults: Insights from machine learning algorithms | |
| A0286: S. Cakar, F. Gokalp Yavuz | |
| Deep learning applications in mental workload classification | |
| A0376: A. Alin | |
| Weighted robust hybrid partial least squares regression forest |
| Parallel session E: COMPSTAT2024 | Wednesday 28.8.2024 | 09:00 - 10:30 |
| Session CI009 (Special Invited Session) | Room: 45 |
| Learning from machine learning by structuring | Wednesday 28.8.2024 09:00 - 10:30 |
| Chair: Stefan Sperlich | Organizer: Stefan Sperlich |
| A0168: M. Scholz, S. Sperlich, G. Cattani | |
| Local machine learning for data giants | |
| A0223: M. Hiabu, J. Meyer, E. Mammen | |
| Random planted forest | |
| A0244: K. Reluga | |
| A complete guide to small area learning |
| Session CO091 | Room: 43 |
| Statistical models and algorithms for survival data | Wednesday 28.8.2024 09:00 - 10:30 |
| Chair: Ambra Macis | Organizer: Ambra Macis |
| A0238: M. Restaino | |
| Feature screening and selection in competing risks models | |
| A0272: T. Sugimoto | |
| Estimation and log-rank testing procedure via bivariate survival copula models under semi-competing risk | |
| A0336: G. Briseno Sanchez, N. Klein, A. Mayr, A. Groll | |
| Boosting distributional copula regression for right-censored bivariate time-to-event data | |
| A0365: T. Matcham | |
| Predicting patient trajectories with deep multi-state models trained on electronic health record data |
| Session CO090 | Room: 44 |
| Advances in functional statistics (HiTEc) | Wednesday 28.8.2024 09:00 - 10:30 |
| Chair: Enea Bongiorno | Organizer: Enea Bongiorno, Kwo Lik Lax Chan |
| A0296: D. Hlubinka, Z. Hlavka, P. Coupek, V. Dolnik | |
| Fourier approach to goodness-of-fit tests for Gaussian random processes | |
| A0333: A. Caponera, A. Stoecker, V. Panaretos | |
| Spherical functional autoregressive models for global aircraft-based atmospheric measurements | |
| A0342: S. Novo, G. Aneiros | |
| fsemipar: An R package for SoF semiparametric regression | |
| A0360: V. Masarotto | |
| Transportation-based change point detection and testing for functional covariances |
| Session CO087 | Room: 052 |
| Reliable and accurate statistical solutions for modern complex data | Wednesday 28.8.2024 09:00 - 10:30 |
| Chair: Samuel Muller | Organizer: Samuel Muller |
| A0267: H. Reimann, S. Moka, G. Sofronov | |
| Continuous optimization for offline change point detection and estimation | |
| A0277: G. Tarr | |
| Visualising model selection stability | |
| A0290: Q. Vu, F. Hui, S. Muller, A. Welsh | |
| Misspecification matters: Prediction under misspecified random effects distributions in GLMMs | |
| A0219: L. Maestrini, J. Scealy, F. Hui, A. Wood | |
| A multiplicative semiparametric regression solution for non-Euclidean data |
| Session CC011 | Room: 001 |
| Time series | Wednesday 28.8.2024 09:00 - 10:30 |
| Chair: Roland Fried | Organizer: COMPSTAT |
| A0169: J.-M. Freyermuth, A. Dudek, D. Dehay | |
| Some contributions to harmonizable time series analysis | |
| A0206: J.M. Bardet, Y.G. Tchabo MBienkeu | |
| Quasi-maximum likelihood estimation of causal linear long memory processes | |
| A0416: R. Strachan, E. Eisenstat | |
| Singular vector autoregressions | |
| A0402: R. Shankar, G. Tarr, I. Wilms, J. Raymaekers | |
| Outlier-robust estimation of state-space models using a penalized approach |
| Session CC140 | Room: 050 |
| Categorical data analysis | Wednesday 28.8.2024 09:00 - 10:30 |
| Chair: Claudia Kirch | Organizer: COMPSTAT |
| A0457: H. Takeshima, J. Tsuchida, H. Yadohisa | |
| Bayesian mixture SEM for ordinal categorical data | |
| A0471: J. Hornicek, Z. Sulc, H. Rezankova, J. Cibulkova | |
| The hierarchical clustering-based method powered by the bootstrap approach for multiple imputations in categorical data | |
| A0464: J. Nakano, N. Shimizu, Y. Yamamoto | |
| A simplification of aggregated symbolic data |
| Session CC134 | Room: 051 |
| Applied and empirical statistics | Wednesday 28.8.2024 09:00 - 10:30 |
| Chair: Andreas Artemiou | Organizer: COMPSTAT |
| A0193: D. Han Aydin | |
| Performance evaluation of some modified maximum likelihood estimators for power function distribution with outliers | |
| A0431: M. Frolova | |
| Employment of tertiary education graduates: International statistical comparisons | |
| A0442: C. Cellan | |
| Uncovering well-being patterns: An archetypal analysis of development and happiness | |
| A0453: J. Sila, L. Kristoufek, J. Kukacka, E. Kocenda | |
| Detecting and understanding wash trading on cryptocurrency exchanges |
| Parallel session F: COMPSTAT2024 | Wednesday 28.8.2024 | 11:00 - 12:30 |
| Session CI010 (Special Invited Session) | Room: 45 |
| Bayesian computational methods | Wednesday 28.8.2024 11:00 - 12:30 |
| Chair: Mattias Villani | Organizer: Mattias Villani |
| A0332: C. Andersson Naesseth | |
| Generative models and approximate Bayesian inference | |
| A0350: C. Robert, R. Ryder, A. Luciano | |
| Insufficient Gibbs sampling | |
| A0504: D. Widmann | |
| Bayesian inference of pharmaceutical models with Pumas: A showcase of Julia for high-performance interactive computing |
| Session CO080 | Room: 43 |
| Advances in distributional regression | Wednesday 28.8.2024 11:00 - 12:30 |
| Chair: Thomas Kneib | Organizer: Thomas Kneib |
| A0307: E. Bergherr, C. Griesbach, M. Lane | |
| Distributional regression for lung function of cystic fibrosis patients with a special focus on center-specific effects | |
| A0294: A. Mayr, A. Stroemer, N. Klein, C. Staerk, G. Briseno Sanchez | |
| Enhanced variable selection for boosting sparser and less complex models in distributional regression | |
| A0320: B. Saefken | |
| Neural distributional regression models | |
| A0465: M. Herp, J. Brachem, T. Kneib, M. Altenbuchinger | |
| Graphical conditional transformation models |
| Session CO108 | Room: 44 |
| Modeling complex data with dependencies (HiTEc) | Wednesday 28.8.2024 11:00 - 12:30 |
| Chair: Sara Taskinen | Organizer: Klaus Nordhausen, Sara Taskinen |
| A0173: F. Hui, M. Samuel, A. Welsh | |
| Adjusted predictions in generalized estimation equations | |
| A0325: B. van der Veen, R. OHara | |
| Fast fitting of phylogenetic random effect models | |
| A0245: H. Zhang, D. Cook, U. Laa, N. Langrene, P. Menendez | |
| Cubble: An R Package for organizing and wrangling multivariate spatio-temporal data | |
| A0372: S. De Iaco | |
| Advances in complex-valued covariance modeling |
| Session CO109 | Room: 051 |
| Computational and statistical methods in clinical research | Wednesday 28.8.2024 11:00 - 12:30 |
| Chair: Maria del Carmen Pardo | Organizer: Maria del Carmen Pardo |
| A0366: C. Staerk, H. Klinkhammer, T. Wistuba, C. Maj, A. Mayr | |
| Issues with the R-squared for the evaluation of polygenic prediction models across diverse ancestries | |
| A0288: M.A. Jacome Pumar, A. Lopez-Cheda, S. Saavedra | |
| Estimating the cure rate in a mixture cure model using presmoothing | |
| A0326: J. de Una-Alvarez | |
| Smoothing spline density estimation from doubly truncated data | |
| A0224: M.D.C. Pardo, A. Franco-Pereira | |
| Relationships between summary ROC indices and overlap coefficients |
| Session CO096 | Room: 052 |
| Optimal experimental design and applications | Wednesday 28.8.2024 11:00 - 12:30 |
| Chair: Frank Miller | Organizer: Frank Miller |
| A0266: C. Tommasi | |
| Discriminating among several random effects models | |
| A0329: R. Harman, L. Filova, S. Rosa | |
| The polytope of optimal approximate designs: Extending the selection of informative experiments | |
| A0192: M. Pilz, M. Kieser | |
| Optimal adaptive two-stage designs | |
| A0371: E. Fackle-Fornius, F. Miller | |
| Optimizing test item calibration - with application to the Swedish national mathematics test |
| Session CC059 | Room: 001 |
| Multivariate data analysis | Wednesday 28.8.2024 11:00 - 12:30 |
| Chair: Bojana Milosevic | Organizer: COMPSTAT |
| A0160: Z. El Hadri | |
| Simple procedure to estimate a structural equation model with latent variables | |
| A0424: K. Tsubotani, J. Tsuchida, H. Yadohisa | |
| Supervised dimension reduction for instrumental variables estimation with some invalid instruments | |
| A0443: A. Monter-Pozos, E. Gonzalez-Estrada | |
| Tests for the multivariate skew-normal distribution based on data transformations | |
| A0154: T. Singh | |
| A sequential method to search for multiple outliers in multivariate data |
| Session CC137 | Room: 050 |
| Practical insights in spatial statistics | Wednesday 28.8.2024 11:00 - 12:30 |
| Chair: Claudia Kirch | Organizer: COMPSTAT |
| A0421: Y. Takemura, F. Ishioka, K. Kurihara | |
| Reliability evaluation of regions within hotspot clusters using hierarchical structure of spatial data | |
| A0422: F. Ishioka, Y. Takemura, K. Kurihara | |
| A novel method for spatial cluster detection in continuous data | |
| A0462: M. Palma, S. Maggio, G. Giungato | |
| Geographically weighted principal components analysis and variography for environmental variables | |
| A0468: R.K. Kyalo, T. Schmid, N. Wuerz | |
| Twofold nested error regression models with data-driven transformation |
| Parallel session G: COMPSTAT2024 | Wednesday 28.8.2024 | 14:00 - 16:00 |
| Session CV060 | Room: 050 |
| Multivariate data analysis and graphical models | Wednesday 28.8.2024 14:00 - 16:00 |
| Chair: George Karabatsos | Organizer: COMPSTAT |
| A0383: E. Iglesias, G. Phillips | |
| A small-sigma approximation for LIML and FLIML to estimation bias in the dynamic simultaneous equation model | |
| A0430: E. Ballante, F.M. Quetti, S. Figini | |
| A Bayesian approach to ensemble clustering | |
| A0408: A. Acharyya, J. Arroyo, M. Clayton, M. Zlatic, Y. Park, C. Priebe | |
| Response prediction with convergence guarantees in multiple random graphs on unknown manifolds | |
| A0486: T.K.H. Nguyen, M. Chiogna, D. Risso | |
| Structure learning for zero-inflated counts, with an application to single-cell RNA sequencing data | |
| A0512: H. Park | |
| A novel computational methodology for clinical characteristic predictive gene network estimation |
| Session CO114 | Room: 43 |
| Statistics on shapes and manifolds | Wednesday 28.8.2024 14:00 - 16:00 |
| Chair: Joern Schulz | Organizer: Joern Schulz |
| A0258: S. Jung | |
| Averaging symmetric positive-definite matrices on the space of eigen-decompositions | |
| A0308: C. von Tycowicz, H.-C. Hege, M. Hanik | |
| Bi-invariant dissimilarity measures for sample distributions in lie groups | |
| A0226: E. Maignant, X. Pennec, A. Calissano | |
| Barycentric subspace analysis of a set of graphs | |
| A0363: B. Eltzner, B. de Groot, M. Habeck, D. Rudolf, J. Hofstadler | |
| Maximum entropy ensemble refinement | |
| A0323: J. Schulz, M. Taheri Shalmani | |
| Statistics on locally parametrized shapes via discrete swept skeletal representations |
| Session CO101 | Room: 45 |
| Variable selection, model selection and nonparametric methods | Wednesday 28.8.2024 14:00 - 16:00 |
| Chair: Marialuisa Restaino | Organizer: Michele La Rocca, Marialuisa Restaino |
| A0200: P. Yu, L. Liang, Y. Zhuang | |
| High-dimensional variable selection in the presence of missing data | |
| A0254: D. Petti, M. Niglio, M. Restaino | |
| BRBVS: variable ranking in copula survival models affected by general censoring scheme | |
| A0304: M. Battauz, P. Vidoni | |
| A boosting method for variance components selection in linear mixed models | |
| A0341: M.G. Schimek | |
| New software developments for the analysis of ranking data | |
| A0354: H. Dai, W. Liang, Y. Wei, H. Huang | |
| Robust inference for the unification of confidence intervals in meta-analysis |
| Session CC046 | Room: 44 |
| Text mining | Wednesday 28.8.2024 14:00 - 16:00 |
| Chair: Francesco Audrino | Organizer: COMPSTAT |
| A0285: B. Prostmaier, B. Gruen, P. Hofmarcher | |
| Seeded Poisson factorization: Leveraging domain knowledge to fit topic models | |
| A0426: E.-J. Senn, M.T. Phan | |
| LongFinBERT: A language model for long financial documents | |
| A0436: J. Schuettler, F. Audrino, F. Sigrist | |
| Does sentiment help in asset pricing? A novel approach using large language models and market-based labels | |
| A0448: K. Inoue, S. Yuki, Y. Terada, H. Yadohisa | |
| Variational inference for the keyword assisted topic models | |
| A0452: N. Cabote | |
| Text data insights and machine learning innovations in monetary policy shock identification |
| Session CC027 | Room: 001 |
| Machine learning | Wednesday 28.8.2024 14:00 - 16:00 |
| Chair: Emese Lazar | Organizer: COMPSTAT |
| A0405: A. Bartonicek | |
| The hidden algebra of interactive visualization: Exploring the links between graphics, statistics, and interaction | |
| A0407: E.L. Saenz Guillen, D. Dimitrova, V. Kaishev | |
| Enhancing geometrically designed spline regression through generalized additive models and functional gradient boosting | |
| A0423: S. Hermes, J. van Heerwaarden, P. Behrouzi | |
| Multi-attribute preferences: A transfer learning approach | |
| A0503: M. Jonker, H. Pazira, E. Massa, T. Coolen | |
| Bayesian federated inference for estimating statistical models based on non-shared multicenter data sets |
| Session CC058 | Room: 051 |
| Computational statistics | Wednesday 28.8.2024 14:00 - 16:00 |
| Chair: Stefanie Biedermann | Organizer: COMPSTAT |
| A0398: M. Mizuta | |
| Derivation of optimal solution by full enumeration for subgroup identification | |
| A0472: I.-I. Roatis, E. Cohen, N. Adams | |
| Categorical encoding as joint optimization in predictive models | |
| A0158: Y. Mao, R. Kessels, T. van der Zanden | |
| Constructing Bayesian optimal designs for discrete choice experiments by simulated annealing | |
| A0451: L. Brusa, F. Bartolucci, F. Pennoni, R. Peruilh Bagolini | |
| A penalized maximum likelihood estimation for hidden Markov models to address latent state separation | |
| A0455: S. Pfahler, P. Georg, R. Schill, M. Klever, L. Grasedyck, R. Spang, T. Wettig | |
| Taming numerical imprecision by adapting the KL divergence to negative probabilities |
| Session CC030 | Room: 052 |
| Forecasting | Wednesday 28.8.2024 14:00 - 16:00 |
| Chair: Thomas Fung | Organizer: COMPSTAT |
| A0172: C.-A. Liu, Y.-T. Chen, J.-H. Su | |
| Bregman model averaging for forecast combination | |
| A0178: B. Kozyrev | |
| Forecast combination and interpretability using random subspace | |
| A0293: J. Frank, J. Dovern | |
| Boosting XGBoost: Using the panel dimension to improve machine-learning-based forecasts in macroeconomics | |
| A0389: A. Haeusser | |
| Univariate time series forecasting using echo state networks: An empirical application | |
| A0418: A. Cerasa, A. Zani | |
| Enhancing electricity price forecasting accuracy: A novel filtering strategy for improved out-of-sample predictions |
| Parallel session H: COMPSTAT2024 | Thursday 29.8.2024 | 09:00 - 10:00 |
| Session CV040 | Room: 051 |
| Biostatistics | Thursday 29.8.2024 09:00 - 10:00 |
| Chair: Andreas Artemiou | Organizer: COMPSTAT |
| A0487: G. Goessler, V. Hofer, H. Manner, W. Goessler | |
| K-cover: A novel way to compare distributions in the context of drug development | |
| A0488: V. Hofer, G. Goessler, H. Manner, W. Goessler | |
| A statistical test for the overlap of normal distributions based on generalized p-values | |
| A0412: S. Ahn | |
| Outlier detection in mass-spectrometry data using the conformal prediction framework |
| Session CO125 | Room: 44 |
| Complex data (HiTEc) | Thursday 29.8.2024 09:00 - 10:00 |
| Chair: Bojana Milosevic | Organizer: Ana Colubi |
| A0395: V. Batagelj | |
| Clustering in multiway networks | |
| A0489: B. Milosevic, J. Radojevic | |
| On the application of kernel-based independence tests to variable selection problems | |
| A0495: R. Alzbutas, T. Iesmantas, J. Sengupta | |
| Enhancing subarachnoid hemorrhage monitoring with AI and uncertainty analysis |
| Session CO094 | Room: 45 |
| Resampling methods for dependent data | Thursday 29.8.2024 09:00 - 10:00 |
| Chair: Claudia Kirch | Organizer: Claudia Kirch |
| A0255: A. Dudek | |
| Optimal choice of bootstrap block length for periodically correlated time series | |
| A0291: C. Jentsch, K. Reichold | |
| A bootstrap-assisted self-normalization approach to inference in cointegrating regressions | |
| A0314: D. Nordman | |
| A practical resampling-based approach to interval estimation for spectral densities |
| Session CC130 | Room: 001 |
| Extreme values | Thursday 29.8.2024 09:00 - 10:00 |
| Chair: Jean Marc Bardet | Organizer: COMPSTAT |
| A0165: M.L. Vidagbandji, A. Berred, C. Bertelle, L. Amanton | |
| Generalized random forest for extreme quantile regression | |
| A0358: C. Mathieu, K. Hees, R. Fried | |
| Modeling waiting times of clustered extreme events with application to mid-latitude winter cyclones | |
| A0220: T. Moriyama | |
| Comparative study on tail probability estimators |
| Session CC136 | Room: 050 |
| Statistical models and inference for applications | Thursday 29.8.2024 09:00 - 10:00 |
| Chair: Thomas Fung | Organizer: COMPSTAT |
| A0177: F.G. Akgul | |
| Inference for multicomponent stress-strength reliability based on generalized Lindley distribution | |
| A0176: F.Z. Dogru, O. Arslan | |
| Inference and diagnostics for a heteroscedastic partially linear model with skew heavy-tailed error distribution | |
| A0440: D. Ferreira, S. Ferreira, P. Antunes, G. Neves | |
| Dynamic linear mixed models for time-dependent data analysis |
| Session CC131 | Room: 052 |
| Copulas in financial econometrics | Thursday 29.8.2024 09:00 - 10:00 |
| Chair: Massimiliano Caporin | Organizer: COMPSTAT |
| A0403: T. Yoshiba, K. Ito | |
| Dynamic asymmetric tail dependence among multi-asset classes for portfolio management: Dynamic skew-t copula approach | |
| A0476: A. Mecchina, R. Pappada, N. Torelli | |
| Copula-based clustering of financial time series via evidence accumulation |
| Session CP001 | Room: 43 |
| Poster Session | Thursday 29.8.2024 09:00 - 10:00 |
| Chair: Marios Demosthenous | Organizer: COMPSTAT |
| A0327: S. Shvydka, V. Sarabeev, M. Zdimalova, M. Ovcharenko | |
| Applying regression methods to model survival data for gammarids (Amphipoda) | |
| A0491: V.Y.-J. Chen, Y.-T. Lu | |
| Geographically weighted logistic quantile regression |
| Parallel session I: COMPSTAT2024 | Thursday 29.8.2024 | 10:30 - 12:30 |
| Session CO077 | Room: 43 |
| Fusing machine learning and statistics | Thursday 29.8.2024 10:30 - 12:30 |
| Chair: Sonja Greven | Organizer: Sonja Greven |
| A0247: M. Wright | |
| Adversarial random forests | |
| A0211: M. Spindler | |
| DoubleMLDeep: Estimation of causal effects with multimodal data | |
| A0263: J. Schmidt-Hieber, S. Langer, G. Clara | |
| Dropout regularization versus L2-penalization in the linear model | |
| A0301: D. Ruegamer | |
| Inference for semi-structured regression | |
| A0302: S. Greven, M. Simnacher, X. Xu, H. Park, C. Lippert | |
| Deep nonparametric conditional independence tests for images |
| Session CO105 | Room: 44 |
| Text mining: Methods and applications (HiTEc) | Thursday 29.8.2024 10:30 - 12:30 |
| Chair: Anna Staszewska-Bystrova | Organizer: Anna Staszewska-Bystrova |
| A0153: V. Naboka-Krell, P. Winker, V. Bystrov, A. Staszewska-Bystrova | |
| Analysing the impact of removing infrequent terms on topic quality in LDA models | |
| A0204: P. Baranowski, S. Wojcik | |
| Textual content and academic journals selectivity: A case of economic journals | |
| A0213: K. Rybinski | |
| Automated question answering for unveiling leadership dynamics in U.S. presidential speeches | |
| A0216: A. Staszewska-Bystrova, V. Bystrov | |
| Goodness-of-fit testing in topic models | |
| A0370: N. Belaid, I. Savin | |
| Startups in African agriculture sector: Insights from the computational linguistics |
| Session CO102 | Room: 45 |
| Clustering and classification for complex data | Thursday 29.8.2024 10:30 - 12:30 |
| Chair: Mika Sato-Ilic | Organizer: Mika Sato-Ilic |
| A0265: S.-K.A. Ng, R. Tawiah, G. McLachlan | |
| The use of mixture models for clustering data with structured dependence | |
| A0300: M. Yamamura, M. Ohishi, H. Yanagihara | |
| On Lasso Poisson regression for categorical variables | |
| A0330: M. Sato-Ilic | |
| Clustering based multidimensional scaling for mixed data | |
| A0338: K. Kirishima, M. Ohishi, R. Oda, K. Okamura, Y. Itoh, H. Yanagihara | |
| Applicability of TreeSHAP to analyze real estate data | |
| A0356: M. Okabe, H. Yadohisa | |
| Classification method for corrupted label data using density ratio |
| Session CO121 | Room: 051 |
| Small area estimation and mixed models | Thursday 29.8.2024 10:30 - 12:30 |
| Chair: Domingo Morales | Organizer: Domingo Morales |
| A0174: S. Sperlich, K. Reluga, G. Claeskens | |
| Post-selection inference for fixed and mixed parameters in linear mixed models | |
| A0236: N. Wuerz, P. Krennmair, T. Schmid, N. Tzavidis | |
| Random forests and mixed effects random forests for small area estimation of general parameters | |
| A0163: M. Bugallo, D. Morales, N. Salvati, F. Schirripa | |
| Temporal M-quantile models and robust bias-corrected small area predictors | |
| A0170: E. Cabello, D. Morales, A. Perez Martin, M.-D. Esteban | |
| Small area estimation of labour force indicators under bivariate Fay-Herriot model with correlated time and area effects | |
| A0481: N. Diz-Rosales, M.J. Lombardia, D. Morales | |
| Improving small area poverty estimates with random-slope mixed models | |
| A0164: D. Morales, M.-D. Esteban, M. Bugallo, M. Marey-Perez | |
| Wildfire prediction using zero-inflated negative binomial mixed models: Application to Spain |
| Session CO123 | Room: 052 |
| Dependence measures | Thursday 29.8.2024 10:30 - 12:30 |
| Chair: Marc-Oliver Pohle | Organizer: Marc-Oliver Pohle |
| A0396: G. Geenens | |
| Universal copulas | |
| A0218: J.-L. Wermuth, M.-O. Pohle | |
| Proper correlation coefficients for discrete random variables | |
| A0379: D. Edelmann, J. Goeman | |
| A Regression Perspective on Generalized Distance Covariance and the Hilbert-Schmidt Independence Criterion | |
| A0269: H. Holzmann, B. Klar | |
| Lancester correlation: A new dependence measure linked to maximum correlation | |
| A0313: M.-O. Pohle, T. Fissler | |
| Generalised covariances and correlations |
| Session CC132 | Room: 001 |
| Financial risk | Thursday 29.8.2024 10:30 - 12:30 |
| Chair: Alessandra Amendola | Organizer: COMPSTAT |
| A0289: H. Kawakatsu | |
| Extracting macro factors in bond risk premia using a supervised method | |
| A0499: R. Peiris, C. Wang, R. Gerlach, M.-N. Tran | |
| Semi parametric financial risk forecasting incorporating multiple realized measures | |
| A0276: L. Garcia-Jorcano, M. Caporin, J.-A. Jimenez-Martin | |
| Diversifying risk parity portfolios with high-frequency principal components | |
| A0390: L. Bauer | |
| Evaluating financial tail risk forecasts with the model confidence set | |
| A0475: L. Petrasek, J. Kukacka | |
| US equity announcement risk premia |
| Session CC022 | Room: 050 |
| Variable and model selection | Thursday 29.8.2024 10:30 - 12:30 |
| Chair: Florian Frommlet | Organizer: COMPSTAT |
| A0234: S. Anatolyev | |
| AIC for many-regressor heteroskedastic regressions | |
| A0473: M. Norouzirad, R. Moura, M. Arashi, F. Marques | |
| Marginalized LASSO in the difference-based partially linear model for variable selection | |
| A0463: A. Caillebotte, E. Kuhn, S. Sarah Lemler | |
| Variable selection and estimation in non-linear mixed-effects models in high dimensional setting | |
| A0262: C. Castel | |
| Comparison of the LASSO and IPF-LASSO methods for multi-omics data: Variable selection with Type I error control | |
| A0387: M. van de Wiel | |
| Shapley values for regression models with interactions |
| Parallel session J: COMPSTAT2024 | Thursday 29.8.2024 | 14:00 - 15:30 |
| Session CI008 (Special Invited Session) | Room: 44 |
| Measure transportation as a tool for statistical inference (HiTEc) | Thursday 29.8.2024 14:00 - 15:30 |
| Chair: Gery Geenens | Organizer: Marc Hallin |
| A0215: H. Shi, M. Hallin, M. Drton, F. Han | |
| Distribution-free tests of multivariate independence based on center-outward signs and ranks | |
| A0306: A. Gonzalez Sanz, M. Hallin, E. del Barrio | |
| Nonparametric multiple-output center-outward quantile regression | |
| A0274: H. Liu, M. Hallin, T. Verdebout | |
| Quantiles and quantile regression on Riemannian manifolds: A measure-transportation-based approach |
| Session CO079 | Room: 43 |
| NLP applications in social sciences (HiTEc) | Thursday 29.8.2024 14:00 - 15:30 |
| Chair: Ivan Savin | Organizer: Ivan Savin |
| A0182: A. Latifi, P. Winker, D. Lenz | |
| Identification of innovation drivers based on technology-related news articles | |
| A0335: J. Birkholz, P. Kerner | |
| The diffusion of green energy narratives | |
| A0352: B. Minuth | |
| Effectiveness of mandatory CSR reporting: Improvement of firms' CSR disclosure information | |
| A0208: I. Savin, J. Birkholz, P. Winker | |
| Exploring political narratives in European elections and their role for election success |
| Session CO093 | Room: 45 |
| Advances in Bayesian deep learning | Thursday 29.8.2024 14:00 - 15:30 |
| Chair: Nadja Klein | Organizer: Nadja Klein |
| A0284: T.-M. Buendert, N. Klein | |
| Partially Bayesian neural networks for adversarial robustness | |
| A0362: A. Immer | |
| Advances in Bayesian neural model selection | |
| A0367: C. Schlauch | |
| Improving motion prediction in autonomous driving with expert knowledge: a Bayesian deep learning approach | |
| A0384: M. Wicker | |
| Provable guarantees for Bayesian neural networks |
| Session CO104 | Room: 051 |
| High dimensional data analysis for social sciences | Thursday 29.8.2024 14:00 - 15:30 |
| Chair: Ida Camminatiello | Organizer: Ida Camminatiello |
| A0282: C. Iorio, A. Gnasso, M. Aria | |
| Inside the black-box models through explainable decision tree ensembles | |
| A0310: P. Sarnacchiaro, I. Ariante | |
| Composite indicators to deep diving into residents perceptions of tourism | |
| A0359: A. Piscitelli, I. Camminatiello | |
| A study for reducing the economic inequalities among European countries | |
| A0316: E. Ivaldi, L.S. Alaimo | |
| Italian subjective well-being: A territorial and longitudinal analysis through a poset methodology |
| Session CO081 | Room: 052 |
| Financial econometrics | Thursday 29.8.2024 14:00 - 15:30 |
| Chair: Roxana Halbleib | Organizer: Roxana Halbleib |
| A0196: P. Adaemmer, S. Lehmann, R.A. Schuessler | |
| Local predictability in high dimensions | |
| A0228: R. Buse, M. Schienle | |
| Predicting Value at Risk for cryptocurrencies with generalized random forests | |
| A0237: P. Sibbertsen, J. Kreye | |
| Testing for a breakdown in forecast accuracy under long memory | |
| A0278: Y. Okhrin | |
| High dimensional change point estimation onthe community structure of networks |
| Session CC143 | Room: 001 |
| Applied statistics and econometrics | Thursday 29.8.2024 14:00 - 15:30 |
| Chair: Marialuisa Restaino | Organizer: COMPSTAT |
| A0425: S.J. Cinco | |
| Clustering the impact: How economic realities and political institutions shaped COVID-19 fiscal responses in Africa | |
| A0511: S. Kharroubi | |
| Use of a nonparametric Bayesian method to model health state preferences: An application to Lebanese SF-6D valuations | |
| A0508: T. Akkaya-Hocagil, L. Ryan, R. Cook, S. Jacobson, J. Jacobson | |
| Benchmark dose profiles for bivariate exposures | |
| A0513: P. Uberti, M.-L. Torrente | |
| Maximum diversification versus minimum risk: Which is better |
| Session CC039 | Room: 050 |
| Biostatistics | Thursday 29.8.2024 14:00 - 15:30 |
| Chair: Andreas Mayr | Organizer: COMPSTAT |
| A0230: M. Mannone, N. Marwan, P. Fazio, P. Ribino | |
| Brain-Network mathematical modeling for neurodegenerative disease | |
| A0251: R. Adatorwovor | |
| A competing risk model for disease-specific or net survival estimation | |
| A0344: A. Mercadie, E. Gravier, G. Josse, N. Vialaneix, C. Brouard | |
| A supervised NMF extension for integrating omics data | |
| A0474: P. Korhonen, F. Hui, S. Taskinen, J. Niku, B. van der Veen | |
| Comparison of joint species distribution models for percent cover data |
| Parallel session K: COMPSTAT2024 | Thursday 29.8.2024 | 16:00 - 17:30 |
| Session CV052 | Room: 001 |
| Complex data analysis | Thursday 29.8.2024 16:00 - 17:30 |
| Chair: Claudia Cappello | Organizer: COMPSTAT |
| A0420: F. Severino, M. Cremona, L. Doroshenko | |
| Functional motif discovery in stock market prices | |
| A0467: A. Mandal | |
| The MEM algorithm and modal clustering of functional data | |
| A0482: H. Gadacha, P. Kubicki, N. Niang | |
| Outlier detection in mixed data | |
| A0483: C. Cappello, S. De Iaco, M. Palma, K. Nordhausen | |
| Different approaches for modeling multivariate space-time data: A performance-based comparison |
| Session CV025 | Room: 050 |
| Applied statistics and empirical analyses | Thursday 29.8.2024 16:00 - 17:30 |
| Chair: Enea Bongiorno | Organizer: COMPSTAT |
| A0500: K. Ayinde | |
| Some new insights into measures of location and adopting voting technique into measures of variability | |
| A0401: M. Fayaz, E. Russo, L. Di Gloria, S. Bertorello, A. Amedei | |
| Classical, Bayesian, and machine learning variable selection methods in colorectal cancer microbiome study: A comparison | |
| A0459: M. Diaz | |
| Comparing curves with the discrete Frechet distance via the family of exponentiated generalized distributions |
| Session CV057 | Room: 051 |
| Computational statistics and applications | Thursday 29.8.2024 16:00 - 17:30 |
| Chair: Thomas Kneib | Organizer: COMPSTAT |
| A0280: K. Robach, S. van der Pas, M. van de Wiel, M. Hof | |
| A stochastic expectation maximisation approach to record linkage | |
| A0414: G. Karabatsos | |
| Copula approximate Bayesian computation using distribution random forests | |
| A0217: T. Zhan, M. Yi, I. Chervoneva | |
| R package QuantileGH: Quantile least Mahalanobis distance estimator for Tukey g-\&-h mixture | |
| A0185: J. Kalina | |
| Highly robust training of regularized radial basis function networks |
| Session CO122 | Room: 43 |
| Advances in finite mixtures for regression and clustering | Thursday 29.8.2024 16:00 - 17:30 |
| Chair: Gabriele Soffritti | Organizer: Gabriele Soffritti |
| A0279: M. Marbac, M. du Roy de Chaumaray | |
| Full model estimation for non-parametric multivariate finite mixture models | |
| A0207: L. Bagnato, A. Punzo, S.D. Tomarchio | |
| Parsimonious mixtures of dimension-wise scaled normal mixtures | |
| A0295: M. Gallaugher | |
| Mixtures of skewed regression models for clustering spatial data | |
| A0361: G. Soffritti, M. Fop, M. Vitelli | |
| Sparse multivariate Gaussian mixture regression with covariance estimation |
| Session CO092 | Room: 44 |
| High-dimensionality and time series (HiTEc) | Thursday 29.8.2024 16:00 - 17:30 |
| Chair: Andreas Artemiou | Organizer: Ana Colubi |
| A0298: J. Zhou | |
| Flexible extreme marginal quantile treatment effect in high dimensions | |
| A0250: L. Soegner, C. Haefke | |
| Fully modified estimation of a quantile cointegration model with a spatial lag | |
| A0260: A. Artemiou | |
| Computationally efficient SVM-based sufficient dimension reduction | |
| A0477: K. Nordhausen, U. Radojicic | |
| Order determination in second-order source separation models using data augmentation |
| Session CO100 | Room: 45 |
| Advances in multivariate distributional regression | Thursday 29.8.2024 16:00 - 17:30 |
| Chair: Guillermo Briseno Sanchez | Organizer: Guillermo Briseno Sanchez, Nadja Klein |
| A0235: R. Haschka | |
| Handling endogenous regressors in quantile regression models: Copula approach without instruments | |
| A0239: L. Kock, N. Klein, D. Nott | |
| Deep mixture of linear mixed models for complex longitudinal data | |
| A0318: V. Gioia, M. Fasiolo, R. Bellio | |
| Generalized additive models for smoothing covariance matrices | |
| A0339: A. Stroemer, N. Klein, A. Mayr | |
| A model-based boosting approach to deal with dependent censoring |
| Session CO107 | Room: 052 |
| Computational methods for statistical learning | Thursday 29.8.2024 16:00 - 17:30 |
| Chair: Mauro Bernardi | Organizer: Mauro Bernardi |
| A0281: A. Panarotto, M. Cattelan, R. Bellio | |
| State-space models for clustering of compositional trajectories | |
| A0340: C. Mattes, W. Hart | |
| Learning algorithms for constrained hidden Markov models | |
| A0393: P.A. Carlesi, M. Bernardi, C. Castiglione, N. Bianco | |
| Dynamical quantile graphical modeling | |
| A0346: L. Gherardini | |
| Dynamic network models with time-varying nodes |
| Parallel session M: COMPSTAT2024 | Friday 30.8.2024 | 09:00 - 10:30 |
| Session CO097 | Room: 43 |
| Recent advances in design and analysis of experiments | Friday 30.8.2024 09:00 - 10:30 |
| Chair: Frederick Kin Hing Phoa | Organizer: Ray-Bing Chen, Frederick Kin Hing Phoa |
| A0179: R.-B. Chen | |
| Optimal exact designs for small studies in toxicology with applications to hormesis via metaheuristics | |
| A0180: F.K.H. Phoa, J.-W. Huang, Y.-H. Chen, Y.-H. Lin, S.P. Lin | |
| An efficient approach for identifying important biomarkers for biomedical diagnosis | |
| A0253: Y.-H. Liao, F.K.H. Phoa, D. Woods | |
| Summary of effect aliasing structure for design selection and factor-column assignment for supersaturated designs | |
| A0271: U. Groemping | |
| Criteria for assessing space filling of a design with emphasis on the stratification pattern |
| Session CO126 | Room: 44 |
| Tutorial: Session I (HiTEc) | Friday 30.8.2024 09:00 - 10:30 |
| Chair: Ivan Savin | Organizer: COST Action HiTeC |
| A0209: I. Savin | |
| Topic modelling |
| Session CO078 | Room: 45 |
| Statistical inference for functional data | Friday 30.8.2024 09:00 - 10:30 |
| Chair: Dominik Liebl | Organizer: Dominik Liebl |
| A0261: S. Otto, L. Winter | |
| Factor-augmented functional regression with an application to electricity price curve forecasting | |
| A0331: F. Telschow, S. Davenport, T. Nichols, A. Schwartzman | |
| Robust FWER control in neuroimaging using random field theory | |
| A0275: C. Fang, D. Liebl | |
| Difference-in-Differences: A Functional data perspective | |
| A0353: A. Stoecker, S. Greven, L. Steyer, M. Pfeuffer | |
| Elastic full Procrustes analysis of plane curves via Hermitian covariance smoothing |
| Session CO095 | Room: 052 |
| Marketing and Innovation | Friday 30.8.2024 09:00 - 10:30 |
| Chair: Yuichi Mori | Organizer: Yuichi Mori |
| A0231: M. So, S.H. Chan, A. Chu | |
| Graphical copula GARCH modelling with dynamic conditional dependence | |
| A0377: Y. Asahi | |
| Analysis of consumer purchasing attitudes toward organically grown vegetables | |
| A0355: A. Nakayama | |
| Applying machine learning approach to marketing uncovering consumer insights through big data | |
| A0357: D. Baier, A. Karasenko, A. Rese | |
| Measuring technology acceptance over time by online customer reviews based transfer learning |
| Session CC034 | Room: 001 |
| Semi- and nonparametric methods | Friday 30.8.2024 09:00 - 10:30 |
| Chair: Stefan Sperlich | Organizer: COMPSTAT |
| A0197: A. De | |
| A non-parametric approach to detect patterns in binary sequences | |
| A0233: C. Valvason, S. Sperlich | |
| Improved confidence intervals with optimal transportation theory | |
| A0494: S. Zhu, A. Celisse | |
| $L^2$-divergence estimator with better finite sample performance | |
| A0466: E. Tomilina, G. Mazo, F. Jaffrezic | |
| Mixed high-dimensional network inference via the Gaussian copula |
| Session CC035 | Room: 050 |
| Clustering | Friday 30.8.2024 09:00 - 10:30 |
| Chair: Maria Brigida Ferraro | Organizer: COMPSTAT |
| A0201: N. Bozkus | |
| Non-decimated lifting based outlier detection algorithm | |
| A0404: C.A. Antonio, J.R. Lansangan | |
| Modified silhouette score for evaluating cluster solutions | |
| A0437: S. Skhosana, S. Millard, F. Kanfer | |
| A new approach to estimate semi-parametric Gaussian mixtures of regressions with varying mixing proportions |
| Session CC139 | Room: 051 |
| Neural networks | Friday 30.8.2024 09:00 - 10:30 |
| Chair: Florian Brueck | Organizer: COMPSTAT |
| A0187: F. Brueck | |
| Generative neural networks for characteristic functions | |
| A0449: A. Saenger, M. Kohler, A. Krzyzak | |
| Learning of deep convolutional network image classifiers via stochastic gradient descent and over-parametrization | |
| A0434: A. Yara, Y. Terada | |
| Nonparametric estimation of conditional class probabilities using deep neural networks |
| Parallel session N: COMPSTAT2024 | Friday 30.8.2024 | 11:00 - 12:00 |
| Session CO103 | Room: 43 |
| Regression functions (HiTEc) | Friday 30.8.2024 11:00 - 12:00 |
| Chair: Matus Maciak | Organizer: Matus Maciak |
| Session CO127 | Room: 44 |
| Tutorial: Session II (HiTEc) | Friday 30.8.2024 11:00 - 12:00 |
| Chair: Ivan Savin | Organizer: COST Action HiTeC |
| A0152: I. Savin | |
| Topic modelling |
| Session CO120 | Room: 45 |
| Stochastic simulation in computational statistics | Friday 30.8.2024 11:00 - 12:00 |
| Chair: David Fernando Munoz | Organizer: David Fernando Munoz |
| Session CO106 | Room: 052 |
| Causal machine learning | Friday 30.8.2024 11:00 - 12:00 |
| Chair: Martin Spindler | Organizer: Martin Spindler |
| A0183: M. Lechner, J. Mareckova | |
| Comprehensive causal machine learning | |
| A0205: K. Kloiber, M. Huber, L. Laffers | |
| Testing identification in mediation and dynamic treatment models | |
| A0415: S. Yuki, K. Tanioka, H. Yadohisa | |
| Multivariate binary extension for W\&A-learner |
| Session CC015 | Room: 001 |
| Computational and financial econometrics | Friday 30.8.2024 11:00 - 12:00 |
| Chair: Francesco Audrino | Organizer: COMPSTAT |
| A0428: J. Chassot, F. Audrino | |
| HARd to beat: The overlooked impact of rolling windows in the era of machine learning | |
| A0328: C. Tezza, L.V. Ballestra | |
| A multi-factor model for pricing commodities when volatility, interest rate and convenience yield are stochastic | |
| A0349: L. Fluri | |
| Dense-to-sparse neural network modelling for financial statement data using feature importance attributions |
| Session CC031 | Room: 050 |
| Statistical modelling | Friday 30.8.2024 11:00 - 12:00 |
| Chair: Tatyana Krivobokova | Organizer: COMPSTAT |
| A0411: B. Liu, O. Grothe, M. Coblenz | |
| Copula estimation with flow copula models | |
| A0413: Y. Lin, S. Nasini, M. Labbe | |
| A generalized voting game for categorical network choices | |
| A0439: S. Ferreira, P. Antunes, D. Ferreira | |
| The four-parameter exponentiated Weibull exponential distribution: Theoretical properties and practical implications |
| Session CC066 | Room: 051 |
| Survival analysis | Friday 30.8.2024 11:00 - 12:00 |
| Chair: Shu-Kay Angus Ng | Organizer: COMPSTAT |
| A0388: P. Oliveira | |
| Inequalities and bounds for order statistics | |
| A0432: B. Monroy-Castillo, I. Van Keilegom, M.A. Jacome, R. Cao | |
| Assessing significance of covariates in mixture cure models using distance correlation | |
| A0501: V. Djeundje | |
| Enhancing dynamic credit scoring with splines specification |
| Parallel session O: COMPSTAT2024 | Friday 30.8.2024 | 12:10 - 13:00 |
| Session CO128 | Room: 44 |
| Tutorial: Session III (HiTEc) | Friday 30.8.2024 12:10 - 13:00 |
| Chair: Ivan Savin | Organizer: COST Action HiTeC |
| A0155: I. Savin | |
| Topic modelling |
| Parallel session P: COMPSTAT2024 | Friday 30.8.2024 | 14:00 - 16:30 |
| Session CO129 | Room: 44 |
| Tutorial: Session IV (HiTEc) | Friday 30.8.2024 14:00 - 16:30 |
| Chair: Ivan Savin | Organizer: COST Action HiTeC |
| A0157: I. Savin | |
| Topic modelling |