KEYNOTE TALKS (GMT+2)
| Keynote talk 1 | Tuesday 23.8.2022 | 09:00 - 10:00 | Room: Aula B |
| Are deviations in a gradually varying mean relevant? | |||
| Speaker: H. Dette | Chair: Maria Brigida Ferraro | ||
| Keynote talk 2 | Thursday 25.8.2022 | 11:30 - 12:20 | Room: Aula B |
| Testing the existence of moments and estimating the tail index of augmented GARCH processes | |||
| Speaker: J.-M. Zakoian Co-authors: C. Francq | Chair: Alessandra Luati | ||
| Keynote talk 3 | Friday 26.8.2022 | 12:10 - 13:15 | Room: Aula B |
| Learning and prediction via hierarchies of random measures in Bayesian nonparametrics | |||
| Speaker: I. Pruenster | Chair: Christophe Croux | ||
PARALLEL SESSIONS (GMT+2)
| Parallel session B: COMPSTAT2022 | Tuesday 23.8.2022 | 10:30 - 12:30 |
| Session CI013 (Special Invited Session) | Room: Aula B |
| Small area estimation | Tuesday 23.8.2022 10:30 - 12:30 |
| Chair: Stefan Sperlich | Organizer: Stefan Sperlich |
| A0604: D. Morales, M.-D. Esteban, M.J. Lombardia, E. Lopez Vizcaino, A. Perez Martin | |
| Empirical best prediction of bivariate nonlinear small area indicators | |
| A0309: F. Schirripa Spagnolo, G. Bertarelli, R. Chambers, D. Haziza, N. Salvati | |
| Full bias correction approaches for M-quantile small area estimators: Application to Italian labour force survey | |
| A0428: Y. Lee, M. Runge, N. Rojas Perilla, T. Schmid | |
| Variable selection using conditional AIC for linear mixed models with data-driven transformations | |
| A0180: S. Sperlich, M.J. Lombardia, K. Reluga | |
| Uniform inference for SAE |
| Session CO073 | Room: Aula D |
| Statistical analysis in finite and infinite dimensional Hilbert spaces | Tuesday 23.8.2022 10:30 - 12:30 |
| Chair: Karel Hron | Organizer: Karel Hron |
| A0328: K. Hron, A. Menafoglio, J. Palarea-Albaladejo, P. Filzmoser, J.J. Egozcue | |
| Weighting of parts in compositional data using Bayes Hilbert spaces | |
| A0356: K. Facevicova, P. Filzmoser, K. Hron | |
| Analysis of multi-factorial compositional data main principles and perspectives | |
| A0319: S. Greven, E.-M. Maier, A. Stoecker, B. Fitzenberger | |
| Additive density-on-scalar regression in Bayes Hilbert spaces with an application to gender economics | |
| A0352: P. Jaskova, K. Hron, J. Palarea-Albaladejo, A. Gaba, Z. Pedisic, D. Dumuid | |
| Scalar-on-function regression and functional isotemporal substitution analysis in the context of time-use data | |
| A0414: H. Nassar, K. Podgorski, R. Basna | |
| Data driven orthogonal basis selection for functional data analysis |
| Session CO166 | Room: Aula G |
| Tutorial I | Tuesday 23.8.2022 10:30 - 12:30 |
| Chair: Peter Winker | Organizer: COMPSTAT |
| A0472: P. Winker | |
| Introductionary tutorial to text mining in econometrics |
| Session CO142 | Room: Aula Q |
| Algebraic statistics | Tuesday 23.8.2022 10:30 - 12:30 |
| Chair: Marta Nai Ruscone | Organizer: Marta Nai Ruscone, Eva Riccomagno |
| A0372: G. Montufar, J. Mueller | |
| Geometry of memoryless policy optimization in POMDPs | |
| A0236: L. Solus | |
| Recent developments in hybrid causal discovery | |
| A0663: C. Amendola, P. Dettling, M. Drton, N.R. Hansen, R. Homs | |
| Identifiability in continuous graphical Lyapunov models | |
| A0497: E. Perrone, R. Fontana | |
| Multivariate Bernoulli distributions and discrete copulas | |
| A0332: F. Rapallo | |
| Markov bases from discrete to continuous frameworks |
| Session CC158 | Room: Aula C |
| Time series | Tuesday 23.8.2022 10:30 - 12:30 |
| Chair: Rob Hyndman | Organizer: COMPSTAT |
| A0297: J.-M. Poggi, Y. Goude, H. Yan, B. Goehry, P. Massart | |
| Random forests for time series | |
| A0299: R. Hyndman | |
| Decomposing time series with complex seasonality | |
| A0390: J. Lee | |
| Testing for and measuring serial dependence by neural networks | |
| A0420: F. Kyriazi, D. Thomakos, J. Guerard | |
| Multivariate adaptive learning forecasting | |
| A0528: M. Gong, R. Killick, C. Nemeth | |
| A changepoint approach to modelling soil moisture dynamics |
| Session CC159 | Room: Aula E |
| Algorithms and computational methods | Tuesday 23.8.2022 10:30 - 12:30 |
| Chair: Bettina Gruen | Organizer: COMPSTAT |
| A0302: F. Brueck | |
| Exact simulation of continuous max-id processes | |
| A0537: M. Schlather, A. Freudenberg | |
| Tricks that accelerate matrix multiplication on CPUs | |
| A0573: A. Freudenberg, M. Schlather | |
| GPU routines for accelerated genomic calculations | |
| A0605: J.M. Pavia | |
| Extending linear programming ecological inference methods by machine learning | |
| A0657: V. Pastukhov | |
| Fussed nearly-isotonic signal approximation |
| Session CC220 | Room: Aula F |
| Computational and financial econometrics II | Tuesday 23.8.2022 10:30 - 12:30 |
| Chair: Niklas Ahlgren | Organizer: COMPSTAT |
| A0588: F. Dunker | |
| Adaptive estimation for somenonparametric instrumental variablemodels with full independence | |
| A0282: F. Violante, S. Grassi | |
| Generalized autoregressive conditional betas | |
| A0519: N. Ahlgren, A. Back, T. Terasvirta | |
| A volatility model with a time-varying intercept | |
| A0669: J. Sila, M. Mark, L. Kristoufek | |
| Forecasting Market Betas In Crypto Markets | |
| A0150: D. Hendry, J.L. Castle, J. Doornik | |
| Discriminating direct from induced equilibrium-mean shifts |
| Session CC151 | Room: Aula H |
| Bayesian statistics | Tuesday 23.8.2022 10:30 - 12:30 |
| Chair: Alicja Jokiel-Rokita | Organizer: COMPSTAT |
| A0422: A. Jokiel-Rokita, R. Magiera | |
| Bayesian estimation versus maximum likelihood estimation in the Weibull-power law process | |
| A0437: F. van der Meulen, M. Schauer | |
| Backward filtering forward guiding for Markov processes | |
| A0448: N. Nakhaeirad, A. Bekker, M. Arashi, S. Millard | |
| A Bayesian nonparametric estimation of entropy for circular data | |
| A0650: B. Majoni, R. Leon-Gonzalez | |
| Exact likelihood for inverse gamma stochastic volatility models |
| Session CC215 | Room: Aula I |
| Classification | Tuesday 23.8.2022 10:30 - 12:30 |
| Chair: Marialuisa Restaino | Organizer: COMPSTAT |
| A0406: S. Golia, M. Carpita | |
| Multi-class classification with imbalanced data: The choice of a categorical classifier | |
| A0419: J. C-Rella, R. Cao, J. Vilar Fernandez | |
| A dimensionality expansion methodology for loss optimization in cost sensitive problems | |
| A0483: D. Eleftheriou | |
| Doping control analysis in athletes steroid profile: A multivariate Bayesian learning approach | |
| A0652: I. Irigoien, C. Arenas | |
| Fuzzy classification with distance-based prototypes | |
| A0594: M. Okabe, H. Yadohisa | |
| Discriminant analysis with corrupted label data using subject similarity |
| Parallel session C: COMPSTAT2022 | Tuesday 23.8.2022 | 14:15 - 15:45 |
| Session CI007 (Special Invited Session) | Room: Aula Q |
| Bootstrap and resampling in cluster analysis | Tuesday 23.8.2022 14:15 - 15:45 |
| Chair: Christian Hennig | Organizer: Christian Hennig |
| A0394: T. Ullmann, C. Hennig, A.-L. Boulesteix | |
| Validation of cluster analysis results on validation data: A systematic framework | |
| A0596: F. Leisch | |
| Resampling methods for exploring cluster stability | |
| A0430: M. Zafer Merhi, Z. Shkedy, A. Essaghir, D. Lin | |
| Clustering of single cell RNAseq data: An integrated analysis using multiple methods and robust clustering solutions |
| Session CO105 | Room: Aula B |
| ISBA session: Applied computational Bayes (virtual) | Tuesday 23.8.2022 14:15 - 15:45 |
| Chair: Giacomo Zanella | Organizer: Daniele Durante, Giacomo Zanella |
| A0222: P. Touloupou, S. Spencer, B. Finkenstadt | |
| Scalable inference for epidemic models with individual level data | |
| A0310: S. Legramanti, D. Durante, P. Alquier | |
| Concentration and robustness of discrepancy-based ABC through Rademacher complexity | |
| A0480: K. McAlinn, M. Kato, S. Sugasawa, K. Takanashi, D. Cabel | |
| Spatially-varying Bayesian predictive synthesis for flexible and interpretable spatial prediction | |
| A0598: C. Robert | |
| Evidence approximation and Bayesian model choice |
| Session CO017 | Room: Aula C |
| Analysis of ranking data | Tuesday 23.8.2022 14:15 - 15:45 |
| Chair: Philip Yu | Organizer: Philip Yu |
| A0350: M. Alvo | |
| Empirical Bayes on a shoestring and other applications | |
| A0290: M.G. Schimek, L. Vitale, B. Pfeifer, M. La Rocca | |
| A computationally efficient non-parametric signal estimation approach for ranking data | |
| A0338: M. Sciandra, A. Plaia, A. Albano | |
| Ensemble methods for item-weighted label ranking: A comparison | |
| A0304: P. Yu, J. Gu | |
| Social order statistics models for ranking data |
| Session CO033 | Room: Aula D |
| Some advances in multivariate and functional statistics | Tuesday 23.8.2022 14:15 - 15:45 |
| Chair: Enea Bongiorno | Organizer: Enea Bongiorno |
| A0237: J. Jacques, M. Amovin, I. Gannaz | |
| Functional data clustering with outlier detection | |
| A0323: S. Nagy | |
| Statistical depth for multivariate and functional data: Recent progress and perspectives | |
| A0384: M. Febrero-Bande, W. Gonzalez-Manteiga, A. Colubi, G. Gonzalez-Rodriguez | |
| The two sample problem for functional data | |
| A0305: A. Goia, E. Bongiorno | |
| Customizing the dimensionality of functional data |
| Session CO045 | Room: Aula E |
| Novel statistical methods for censored and skew data | Tuesday 23.8.2022 14:15 - 15:45 |
| Chair: Victor Hugo Lachos Davila | Organizer: Victor Hugo Lachos Davila |
| A0153: F. Louzada | |
| A repairable system subjected to hierarchical competing risks: Modeling and applications | |
| A0216: C.E. Galarza Morales, K. Valeriano, L. Matos | |
| Moments and random number generation for the truncated elliptical family of distributions | |
| A0325: A. Roy, T. Opheim | |
| Linear models for multivariate repeated measures data from a skew normal distribution | |
| A0536: F. Schumacher, K.A. Loor Valeriano, V.H. Lachos Davila, L. Avila Matos, C.E. Galarza Morales | |
| Censored autoregressive regression modeling using the R package ARCensReg |
| Session CO125 | Room: Aula I |
| Statistical analysis of networks: Applications in cyber-security | Tuesday 23.8.2022 14:15 - 15:45 |
| Chair: Francesco Sanna Passino | Organizer: Francesco Sanna Passino |
| A0280: K. Highnam | |
| Computer network security datasets | |
| A0365: A. Mantziou, F. Sanna Passino, N. Heard, P. Thiede, R. Bevington | |
| Unsupervised attack pattern detection in cyber-security using topic modelling | |
| A0379: I. Gallagher, P. Rubin-delanchy, C. Priebe, A. Jones, A. Bertiger | |
| Spectral embedding of weighted graphs | |
| A0707: H. Helfer Hoeltgebaum, N. Adams, C. Fernandes | |
| Estimation, forecasting and anomaly detection for nonstationary streams using adaptive estimation |
| Session CO115 | Room: Virtual Room R1 |
| Latent variable and psychometric modelling (virtual) | Tuesday 23.8.2022 14:15 - 15:45 |
| Chair: Michela Battauz | Organizer: Michela Battauz |
| A0210: M. Wiberg | |
| Equating tests with mixed format tests | |
| A0211: S. Bacci, B. Bertaccini, F. Cipollini | |
| Monitoring the Brunelleschi's Dome through latent variable models | |
| A0451: R. Di Mari | |
| Psycho COVID-19: Evaluating the risk of the psycho-physical impact of the pandemic | |
| A0634: G. Alfonzetti | |
| A stochastic optimization algorithm for pairwise likelihood estimation of factor models with ordinal data |
| Session CC219 | Room: Aula F |
| Feature selection and variable importance | Tuesday 23.8.2022 14:15 - 15:45 |
| Chair: Karel Hron | Organizer: COMPSTAT |
| A0415: B. Liquet, S. Moka, H. Zhu, S. Muller | |
| Best subset selection via continuous optimization | |
| A0506: N. Hernandez, G. Martos | |
| Domain selection for Gaussian processes | |
| A0221: C.J. Salaroli, M.D.C. Pardo | |
| Features selection and combination in high-dimensional data with the penalized Youden index | |
| A0447: M. Medl, M. Medl, T. Scharl, A. Duerauer, F. Leisch | |
| Permutation based variable importance determination for deep learning |
| Session CC162 | Room: Aula G |
| Parametric inference | Tuesday 23.8.2022 14:15 - 15:45 |
| Chair: Sara Taskinen | Organizer: COMPSTAT |
| A0270: T. Massing | |
| Parametric estimation of tempered stable laws | |
| A0344: A. Hernandez | |
| Geometric goodness of fit measure to detect patterns in data point clouds | |
| A0505: M. Whitehouse | |
| Fast and consistent inference in compartmental models of epidemics using Poisson approximate likelihoods | |
| A0626: L. McQuaid, S. Moghaddam, K. Burke | |
| Penalized power-generalized Weibull distributional regression |
| Session CC157 | Room: Aula H |
| Applied statistics and data analysis | Tuesday 23.8.2022 14:15 - 15:45 |
| Chair: Qing Pan | Organizer: COMPSTAT |
| A0465: B. Santos | |
| Comparing dominance of tennis' big three via multiple-output Bayesian quantile regression models | |
| A0478: C. Spychala, C. Goga, C. Dombry | |
| Spatial modelling road accidents in Besancon (France) using log-gaussian cox processes | |
| A0584: A. Skolkova | |
| Elastic-net for instrumental variables regression | |
| A0658: F. Gokalp Yavuz | |
| Data science education for developing countries: The process of democratization |
| Parallel session D: COMPSTAT2022 | Tuesday 23.8.2022 | 16:15 - 17:45 |
| Session CV193 | Room: Aula B |
| Applied statistics (virtual) | Tuesday 23.8.2022 16:15 - 17:45 |
| Chair: Anuradha Roy | Organizer: COMPSTAT |
| A0502: A. Guizzardi, L.V. Ballestra, E. DInnocenzo | |
| A statistical approach to evaluate last minute pricing decisions in the online hotel market | |
| A0592: K. Young, L. Bantis | |
| Estimation and inference on the partial volume under the ROC surface | |
| A0636: N. Chakraborty, T. Mahmood | |
| Failure rate monitoring in generalized gamma-distributed process | |
| A0614: I. Kenny, D. Kbaier | |
| A novel environmental system-focused empirical mode decomposition analysis: Application to Minas passage |
| Session CI015 (Special Invited Session) | Room: Aula F |
| Bayesian and computational extreme value analysis | Tuesday 23.8.2022 16:15 - 17:45 |
| Chair: Miguel de Carvalho | Organizer: Miguel de Carvalho |
| A0287: B. Sanso, P. Trubey | |
| A Bayesian non-parametric approach for multivariate peak over threshold models and anomaly detection | |
| A0383: M. de Carvalho, P. de Zea Bermudez | |
| An extreme value Bayesian Lasso for the conditional bulk and tail | |
| A0386: E. Hector | |
| Distributed inference for extreme value analysis of large spatial datasets |
| Session CO031 | Room: Aula C |
| Statistical text mining | Tuesday 23.8.2022 16:15 - 17:45 |
| Chair: Peter Winker | Organizer: Peter Winker |
| A0335: V. Naboka-Krell, P. Winker, P. Tillmann, A. Latifi | |
| Measuring fiscal policy preferences based on the German Bundestag speeches and public discourse | |
| A0515: V. Bystrov, V. Naboka-Krell, A. Staszewska-Bystrova, P. Winker | |
| Comparative analysis of LDA model selection criteria based on Monte Carlo simulations | |
| A0342: A. Latifi, P. Winker, D. Lenz | |
| Identification of innovation diffusion trends with FDA clustering | |
| A0570: I. Savin | |
| A survey of scientists opinions on climate mitigation policy |
| Session CO123 | Room: Aula D |
| Statistical analysis of networks | Tuesday 23.8.2022 16:15 - 17:45 |
| Chair: Francesco Sanna Passino | Organizer: Francesco Sanna Passino |
| A0345: N. Heard | |
| Changepoint inference with a graphical dependence structure | |
| A0358: A. Modell | |
| Spectral clustering under degree heterogeneity with the random walk Laplacian | |
| A0385: J. Agterberg, J. Arroyo, Z. Lubberts | |
| Community detection in multilayer degree-corrected stochastic blockmodels | |
| A0666: P. Rubin-delanchy | |
| Manifold structure in graph embeddings |
| Session CO095 | Room: Aula E |
| Statistical learning in practice | Tuesday 23.8.2022 16:15 - 17:45 |
| Chair: Alejandro Murua | Organizer: Alejandro Murua |
| A0590: R. Maitra, C. Llosa-Vite | |
| Reduced-rank tensor-on-tensor regression and tensor-variate analysis of variance | |
| A0591: K. Dorman, Y. Zhang, H.T.H. Vu, G. Tuteja | |
| Unsupervised deep learning of ATAC-seq peaks | |
| A0501: T. Dimitriadis, T. Gneiting, A. Jordan, P. Vogel | |
| Evaluating probabilistic classifiers: The Triptych | |
| A0599: F. Maire | |
| Independent Metropolis sampler without rejection |
| Session CO131 | Room: Aula G |
| Analysis of complex real life data | Tuesday 23.8.2022 16:15 - 17:45 |
| Chair: Qing Pan | Organizer: Qing Pan |
| A0412: Q. Pan | |
| Risk predictions using panel count data with informative observation times | |
| A0435: Y. Li | |
| Informed presence in electronic health record data: Bias and bias reduction approaches in longitudinal analyses | |
| A0421: A. Ciarleglio | |
| Multiple imputation methods for functional data with applications in mental health research | |
| A0190: A. Birbilas, A. Rackauskas | |
| Functional modeling of telecommunications data |
| Session CO103 | Room: Aula I |
| Statistical methods for survival data | Tuesday 23.8.2022 16:15 - 17:45 |
| Chair: Marialuisa Restaino | Organizer: Sara Milito, Marialuisa Restaino |
| A0417: C. Moreira | |
| Bias induced by ignoring double truncation | |
| A0522: M. Restaino, S. Milito | |
| A note on nonparametric survival functions under censored and truncated data | |
| A0556: D. Dobler | |
| Relative treatment effects in two dependent samples: An alternative to logrank or sign tests | |
| A0695: F. Hooti, J. Ahmadi, M. Longobardi | |
| General proportional mean residual and past lifetime frailty models |
| Session CO176 | Room: Aula Q |
| Dimension reduction in recent cross sectional and time series methods | Tuesday 23.8.2022 16:15 - 17:45 |
| Chair: Matteo Farne | Organizer: Matteo Farne |
| A0434: A. Aue, H. Li, D. Paul, J. Peng | |
| Testing high-dimensional general linear hypotheses under a multivariate regression model with spiked noise covariance | |
| A0517: V. Characiejus, C. Cerovecki, S. Hoermann | |
| The maximum of the periodogram of a sequence of functional data | |
| A0670: M. Farne, M. Barigozzi | |
| An algebraic estimator for large spectral density matrices | |
| A0525: M. Bogdan, X. Dupuis, P. Graczyk, B. Kolodziejek, T. Skalski, P. Tardivel, M. Wilczynski | |
| Pattern recovery by SLOPE |
| Session CC160 | Room: Aula H |
| Machine learning and data science | Tuesday 23.8.2022 16:15 - 17:45 |
| Chair: Elisa Perrone | Organizer: COMPSTAT |
| A0252: R. Abbasi Asl | |
| Compression-enabled interpretability of deep learning models for scientific discovery | |
| A0533: D. Williams, S. Liu | |
| Kernelised Stein discrepancy for truncated density estimation | |
| A0564: M. Reeves, H. Bhat | |
| Estimating continuous-time Markov chain transition rate functions with neural networks | |
| A0578: L. Pacchiardi, R. Dutta | |
| Generative neural networks via scoring rule minimization for probabilistic forecasting and likelihood-free inference |
| Parallel session E: COMPSTAT2022 | Tuesday 23.8.2022 | 17:55 - 18:55 |
| Session CO164 | Room: Aula B |
| Biomedical research on biomarkers: Methods \& applications (virtual) | Tuesday 23.8.2022 17:55 - 18:55 |
| Chair: Laura Antolini | Organizer: Laura Antolini, Stefania Galimberti |
| A0330: F. Bovis | |
| Personalized response to treatment in patients with MS: Do different patients show benefits on different outcomes? | |
| A0382: G. Cortese, T. Scheike | |
| Estimation of the marginal mean of recurrent events | |
| A0600: G. Infante, F. Ambrogi, R. Miceli | |
| Sample size and predictive performance of machine learning methods with survival data. |
| Session CO085 | Room: Aula D |
| Applied data science and statistical learning | Tuesday 23.8.2022 17:55 - 18:55 |
| Chair: Frederic Bertrand | Organizer: Frederic Bertrand |
| A0314: Y. Valero, L.A. Ebongue Ebaha, F. Bertrand, M. Maumy | |
| Reinforcement learning for next best action recommendation in process data | |
| A0320: E. Logosha, F. Bertrand, M. Maumy | |
| Sensitivity analysis using discrete event simulation on the selling times of a fraction of a stock | |
| A0552: F. Fahs, F. Bertrand, M. Maumy-Bertrand | |
| Forecasting electricity consumption at household level |
| Session CO109 | Room: Aula E |
| Dynamic models for discrete time series and longitudinal data | Tuesday 23.8.2022 17:55 - 18:55 |
| Chair: Roberto Di Mari | Organizer: Roberto Di Mari |
| A0377: S. Mildiner Moraga, E. Aarts | |
| Extending the Poisson hidden Markov model to the multilevel framework with individual random effects | |
| A0453: M. Doretti, G.E. Montanari, F. Bartolucci, M.F. Marino | |
| Evaluating complex agency effects on status transitions: Challenges within a Latent Markov Model paradigm | |
| A0351: P. Cizek | |
| Bias-corrected robust estimation of dynamic panel data models |
| Session CO180 | Room: Aula F |
| Computational statistics from the lens of young researchers II | Tuesday 23.8.2022 17:55 - 18:55 |
| Chair: Riccardo Ceccato | Organizer: Riccardo Ceccato, Marta Disegna |
| A0225: J. Beck, A. Bathke | |
| A unifying framework for rank and pseudo-rank based inference using nonparametric confidence distributions | |
| A0343: E. Barzizza, R. Arboretti, N. Biasetton, M. Disegna, L. Pegoraro, L. Salmaso | |
| Permutation tests for C-sample problems: A multivariate scenario | |
| A0426: L. Pegoraro, R. Arboretti, E. Barzizza, N. Biasetton, R. Ceccato, M. Disegna, L. Salmaso | |
| A novel active learning criterion for experiments with multiple responses |
| Session CO146 | Room: Aula Q |
| IFCS session: Assessment of cluster stability and phylogenetic inference | Tuesday 23.8.2022 17:55 - 18:55 |
| Chair: Berthold Lausen | Organizer: Berthold Lausen |
| A0443: C. Hennig, S. Akhanli | |
| Using aggregated cluster validity indexes to cluster football players performance data | |
| A0469: A.F. Leuchtenberger, S.M. Crotty, T. Drucks, H.A. Schmidt, S. Burgstaller-Muehlbacher, A. von Haeseler | |
| Phylogeny and artificial neural networks | |
| A0676: B. Lausen | |
| Parametric bootstrap evaluation of unsupervised statistical learning and applications |
| Session CC229 | Room: Aula C |
| Missing data | Tuesday 23.8.2022 17:55 - 18:55 |
| Chair: Victor Hugo Lachos Davila | Organizer: COMPSTAT |
| A0557: G. Frahm | |
| Missing-data analysis with power M-estimators | |
| A0646: M. AL-Shaaibi, R. Wesonga | |
| An R Package for bias reduction with LogF(1,1) penalty under the MAR mechanism | |
| A0654: J. Noonan, R. Mitra, S. Biedermann | |
| Improving the power of a test for detecting ``missing not at random'' |
| Session CC223 | Room: Aula G |
| Forecasting | Tuesday 23.8.2022 17:55 - 18:55 |
| Chair: Aldo Goia | Organizer: COMPSTAT |
| A0563: A. Bucci, L. Ippoliti, P. Valentini | |
| Forecasting cardiorespiratory hospitalizations from air pollution levels through artificial neural networks | |
| A0243: S. Safi | |
| On predicting growth factor of daily new cases data of COVID-19 epidemic in Italy using ARIMA-ANN hybrid model | |
| A0575: R. Miglio, G. Roli, M. Scagliarini | |
| A joint use of monitoring and forecasting methods to detect change points in daily hospitalizations |
| Session CC230 | Room: Aula H |
| Statistical modelling and inference | Tuesday 23.8.2022 17:55 - 18:55 |
| Chair: Francisco Louzada | Organizer: COMPSTAT |
| A0690: M. Manca, F. Bertolino, S. Columbu, M. Musio | |
| The Bayesian discrepancy measure: A new method for Bayesian inference | |
| A0691: I. Pereira | |
| Bayesian modeling of time series of counts under censoring | |
| A0696: M. Bhaduri | |
| Detecting breaks in certain random intensities through sequential testing on point processes |
| Session CC217 | Room: Aula I |
| Mixed models and applications | Tuesday 23.8.2022 17:55 - 18:55 |
| Chair: Domingo Morales | Organizer: COMPSTAT |
| A0223: D. Ferreira, S. Ferreira, C. Nunes, J. Mexia | |
| Nesting random effects factors in fixed effects factors | |
| A0224: S. Ferreira, D. Ferreira, C. Nunes, J. Mexia | |
| A simulation study considering mixed linear models with cumulants generated by a Weibull distribution | |
| A0585: A.T. Stueber, S. Coors, K. Jeblick, A. Mittermeier, O. Oecal, B. Schachtner, P. Wesp, M. Seidensticker, M. Ingrisch | |
| ML pipeline for radiomics-based survival analysis on CT images of patients with hepatic CRC metastases |
| Parallel session F: COMPSTAT2022 | Wednesday 24.8.2022 | 09:00 - 10:30 |
| Session CV191 | Room: Aula B |
| Semi- and nonparametric methods (virtual) | Wednesday 24.8.2022 09:00 - 10:30 |
| Chair: Dennis Dobler | Organizer: COMPSTAT |
| A0620: M. Taku | |
| Nonparametric distribution estimators of sample maximum in iid settings | |
| A0625: M. Kitani, Y. Ma, H. Murakami | |
| Two-sample modified Anderson-Darling test and its properties | |
| A0273: K. Huang, S. Zheng, L. Yang | |
| Inference for dependent error functional data with application to event related potentials | |
| A0269: Z. Song, L. Yang, Y. Zhang | |
| Hypotheses testing of functional principal components |
| Session CI009 (Special Invited Session) | Room: Aula G |
| Non-regular statistical analytics for non-normal data | Wednesday 24.8.2022 09:00 - 10:30 |
| Chair: Tsung-I Lin | Organizer: Tsung-I Lin |
| A0374: G. McLachlan, S. Lee | |
| Some skew distributions useful in model-based clustering | |
| A0265: V.H. Lachos Davila, L. Avila Matos, F. Schumacher | |
| skewlmm: An R Package for fitting skewed and heavy-tailed linear mixed models | |
| A0207: M. Arashi, M. Taavoni | |
| High-dimensional generalized linear model for longitudinal data |
| Session CO063 | Room: Aula C |
| Copula models and applications | Wednesday 24.8.2022 09:00 - 10:30 |
| Chair: Elisa Perrone | Organizer: Elisa Perrone |
| A0534: C. Grazian, C. Villa, L. Brunero, D. Battagliese | |
| Copula modelling with penalised complexity priors | |
| A0471: S. Fuchs | |
| Quantifying directed dependence via dimension reduction | |
| A0572: S. Saminger-Platz, A. Kolesarova, A. Seliga, R. Mesiar, E.P. Klement | |
| Dependence parameters of some perturbation-based copulas | |
| A0283: C. Garcia-Gomez, A. Perez Espartero, M. Prieto-Alaiz | |
| The evolution of poverty in the EU-28: a further look based on multivariate tail dependence |
| Session CO069 | Room: Aula D |
| Inference for functional data | Wednesday 24.8.2022 09:00 - 10:30 |
| Chair: Dominik Liebl | Organizer: Dominik Liebl |
| A0391: L. Wegner, M. Wendler | |
| Robust detection for change-points in functional time series based on spatial signs and bootstrap | |
| A0334: S. Davenport | |
| Confidence regions for the location of peaks of a smooth random field | |
| A0473: T. Mensinger | |
| Fair causal inference with functional data | |
| A0597: F. Telschow, A. Schwartzman | |
| Simultaneous inference with CoPE sets |
| Session CO059 | Room: Aula Q |
| Advances in latent variable models I (virtual) | Wednesday 24.8.2022 09:00 - 10:30 |
| Chair: Paolo Giordani | Organizer: Paolo Giordani |
| A0312: X. Song | |
| Causal mediation analysis with latent mediators and survival outcome | |
| A0336: M. Battauz | |
| A fused lasso penalization for the nominal response model | |
| A0359: K. Van Deun | |
| Joint sparse principal component analysis | |
| A0499: S. Pandolfi, F. Bartolucci, F. Pennoni | |
| A misspecification test for hidden Markov models based on finite mixture models |
| Session CC213 | Room: Aula E |
| Model-based clustering | Wednesday 24.8.2022 09:00 - 10:30 |
| Chair: Francesco Sanna Passino | Organizer: COMPSTAT |
| A0440: T. Scharl, B. Gruen | |
| Modelling three-way RNA sequencing data using matrix-variate Gaussian mixture models | |
| A0542: F. Amato, J. Jacques | |
| Clustering longitudinal ordinal data | |
| A0545: L. Brusa, C. Matias | |
| A stochastic block model for hypergraphs | |
| A0503: G. Marchello, M. Corneli, C. Bouveyron | |
| A dynamic latent block model for co-clustering of zero-inflated count data streams |
| Session CC218 | Room: Aula F |
| Design of experiments | Wednesday 24.8.2022 09:00 - 10:30 |
| Chair: Frederick Kin Hing Phoa | Organizer: COMPSTAT |
| A0268: K. Zhu, H. Liu | |
| Pair-switching rerandomization | |
| A0274: S.-F. Tsai | |
| Constructing optimal order-of-addition designs using latin squares | |
| A0397: S. Leorato, C. Tommasi, A. Lanteri, J. Lopez-Fidalgo | |
| Optimal design to test for heteroscedasticity in a regression model | |
| A0479: A. Mahmoudi, S. Mandal | |
| A comparative study of methods for constructing optimal designs |
| Session CC222 | Room: Aula H |
| Biostatistics and applications | Wednesday 24.8.2022 09:00 - 10:30 |
| Chair: Malgorzata Bogdan | Organizer: COMPSTAT |
| A0396: C.J. Perez Sanchez, J. Carron, Y. Campos-Roca, M. Madruga Escalona | |
| A computer-aided diagnosis system to detect Parkinson disease by using acoustic features | |
| A0539: M. Borghesi, S. Bonnini | |
| Multivariate regression model and permutation MANOVA: Case study on mental health effects of covid-19 lockdown | |
| A0664: T. Park, O. Kwon, C. Lee | |
| Statistical modeling of Health space based on metabolic stress and oxidative stress scores | |
| A0454: M. Paries, E. Vigneau, S. Bougeard | |
| Multiblock analysis of mixed data with optimal scaling: Application in epidemiology |
| Session CC152 | Room: Aula I |
| Robust methods I | Wednesday 24.8.2022 09:00 - 10:30 |
| Chair: Peter Filzmoser | Organizer: COMPSTAT |
| A0348: A. van der Merwe, J. Ferreira | |
| Computational discussions within an integer time series setup using a novel Poisson-Lindley model | |
| A0398: P. Janacek, J. Kalina | |
| A bootstrap comparison of robust regression estimators | |
| A0401: A. Minasyan, A. Dalayan | |
| All-in-one Robust Estimator of the Gaussian Mean | |
| A0494: P. Pfeiffer, A. Alfons, P. Filzmoser | |
| Efficient computation of robust multivariate maximum association |
| Session CP001 | Room: Virtual Posters Room I |
| Poster session I | Wednesday 24.8.2022 09:00 - 10:30 |
| Chair: Cristian Gatu | Organizer: COMPSTAT |
| Parallel session G: COMPSTAT2022 | Wednesday 24.8.2022 | 11:00 - 12:30 |
| Session CV197 | Room: Aula Q |
| Statistical modelling and inference (virtual) | Wednesday 24.8.2022 11:00 - 12:30 |
| Chair: Fabrizio Durante | Organizer: COMPSTAT |
| A0653: M. Bernardi, F. Lisi | |
| Multiple non-crossing quantiles models for density forecasting | |
| A0638: C. Li | |
| Effect of censoring time on the statistical monitoring of lifetime data | |
| A0647: J. Park, T. Choi | |
| Bayesian semiparametric copula estimation and model selection: A comparison study | |
| A0661: L. Donayre | |
| Constructing likelihood-ratio-based confidence intervals for multiple threshold parameters |
| Session CI011 (Special Invited Session) | Room: Aula F |
| Multistate models and intermediate events | Wednesday 24.8.2022 11:00 - 12:30 |
| Chair: Martina Mittlboeck | Organizer: Martina Mittlboeck |
| A0203: L. Machado | |
| Analysis of survival data with multiple events: New contributions and practical recommendations | |
| A0531: L. Antolini, E. Tassistro, D.P. Bernasconi, P. Rebora, M.G. Valsecchi | |
| Modelling the hazard of transition into the absorbing state in the illness-death survival model | |
| A0554: U. Poetschger, H. Heinzl, M. Mittlboeck | |
| Evaluating longterm survival with generalised and weighted pseudovalues in paediatric stem cell transplantation studies |
| Session CO057 | Room: Aula B |
| Bayesian time series novelty (virtual) | Wednesday 24.8.2022 11:00 - 12:30 |
| Chair: Michele Costola | Organizer: Luca Rossini |
| A0175: J. Cross, A. Poon, G. Koop, C. Hou | |
| Large vector autoregressions with stochastic volatility in mean | |
| A0444: A. Camehl, D. Fok, K. Gruber | |
| A general Bayesian approach to multiple-output quantile regression | |
| A0468: K. Klieber, F. Huber, N. Hauzenberger, M. Marcellino | |
| Model specification for Bayesian neural networks in macroeconomics | |
| A0514: M. Costola, M. Iacopini, C. Wichers | |
| Time-varying multilayer networks in a Bayesian spatial autoregressive model |
| Session CO097 | Room: Aula C |
| Sports statistics | Wednesday 24.8.2022 11:00 - 12:30 |
| Chair: Leonardo Egidi | Organizer: Leonardo Egidi |
| A0245: L. Grassetti | |
| G-RAPM: Revisiting players contributions in regularized adjusted plus-minus models for basketball analytics | |
| A0354: A. Groll | |
| Modeling and predicting the UEFA EURO 2020 with hybrid machine learning | |
| A0370: A. Lubisco | |
| Is the man-up situation really effective in women's waterpolo? A study on the 2020 European Championship | |
| A0632: S.M. Nagarajan, A. Maes, D. Goossens, L.M. Hvattum, C. Ley | |
| Plus-minus a couple of millions: A machine learning model for transfer fee analysis |
| Session CO170 | Room: Aula D |
| Volatility models | Wednesday 24.8.2022 11:00 - 12:30 |
| Chair: Jean-Michel Zakoian | Organizer: Christian Francq |
| A0244: B.M. Kandji, C. Francq, J.-M. Zakoian | |
| Inference on multiplicative component GARCH without any small-order moment | |
| A0278: C. Francq, F. Blasques, S. Laurent | |
| Linear regressions on time series | |
| A0277: J. Royer, C. Francq, J.-M. Zakoian | |
| A multivariate ARCH($\infty$) model with exogenous variables and dynamic conditional betas | |
| A0289: A. Aknouche, C. Francq | |
| An extended GARCH model with two volatility sequences |
| Session CO091 | Room: Aula I |
| Advances in latent variable models II (virtual) | Wednesday 24.8.2022 11:00 - 12:30 |
| Chair: Paolo Giordani | Organizer: Paolo Giordani |
| A0253: A. Ernst, M. Timmerman, F. Ji, B. Jeronimus, C. Albers | |
| Uncovering clusters and within-cluster variation in time series: Mixture multilevel vector-autoregressive modeling | |
| A0411: M.F. Marino, M. Alfo, M.G. Ranalli, N. Salvati | |
| lqmix: An R package to model longitudinal data via mixtures of linear quantile regressions | |
| A0429: S. Cagnone, S. Bianconcini | |
| Approximate likelihood estimation of dynamic latent variable models for count data | |
| A0509: S. Taskinen, P. Korhonen, F. Hui, J. Niku | |
| Fast and universal estimation of latent variable models using extended variational approximations |
| Session CC208 | Room: Aula E |
| Change-point detection | Wednesday 24.8.2022 11:00 - 12:30 |
| Chair: Berthold Lausen | Organizer: COMPSTAT |
| A0194: M. Pesta, M. Maciak, B. Pestova | |
| Changepoint in dependent and non-stationary panels | |
| A0492: J. Flossdorf, C. Jentsch, R. Fried | |
| Change detection in dynamic networks using flexible multivariate control charts | |
| A0561: G. Agarwal, I. Eckley, P. Fearnhead | |
| Modelling and detecting changes in spatial time series | |
| A0496: O. Li, R. Killick | |
| Changepoint detection in periodic behaviour |
| Session CC212 | Room: Aula G |
| Data depth | Wednesday 24.8.2022 11:00 - 12:30 |
| Chair: Stanislav Nagy | Organizer: COMPSTAT |
| A0381: G. Van Bever, G. Louvet | |
| The influence function of scatter halfspace depth | |
| A0466: R. Dyckerhoff, S. Nagy, P. Laketa | |
| Efficient computation of the angular halfspace depth | |
| A0530: A. Castellanos, P. Mozharovskyi, F. d Alche-Buc | |
| RKHS-based projection depths | |
| A0577: P. Mozharovskyi, R. Dyckerhoff, S. Nagy | |
| Approximate computation of projection depths |
| Session CC209 | Room: Aula H |
| Text mining | Wednesday 24.8.2022 11:00 - 12:30 |
| Chair: Peter Winker | Organizer: COMPSTAT |
| A0220: D. Dayta, E. Barrios | |
| Semiparametric latent topic modeling on consumer-generated corpora | |
| A0316: D. Eugenidis | |
| Gender differences in personality perceptions in the labor force: Use of new data sources | |
| A0618: L. Kontoghiorghes, A. Colubi | |
| Testing the equality of topic distribution between documents of a corpus | |
| A0388: C. Funk, E. Toenjes, L. Breuer, R. Teuber | |
| Difference in SDG reportings of research articles using zero-shot text classification |
| Parallel session H: COMPSTAT2022 | Wednesday 24.8.2022 | 14:15 - 16:15 |
| Session CV195 | Room: Aula B |
| Algorithms and computational methods (virtual) | Wednesday 24.8.2022 14:15 - 16:15 |
| Chair: Paolo Giordani | Organizer: COMPSTAT |
| A0487: R. Azais, F. Ingels | |
| Enumeration of substructures in convolution kernels for structured data: the case of the subtree kernel | |
| A0583: P.C.R. Vicente | |
| Evaluating the effect of planned missing designs in the structural equation models fit measures | |
| A0240: J. Buescu, J. Buescu | |
| Mean of exponential distributions: Estimation from sums of unequal size samples | |
| A0311: M. Cremona, H. Dang, F. Chiaromonte | |
| smoothEM: A new approach for the simultaneous assessment of smooth curves and spikes | |
| A0294: K.W. Chan, M.F. Leung | |
| On variance estimation in online problems |
| Session CO140 | Room: Aula C |
| Computational statistics: Theory and applications | Wednesday 24.8.2022 14:15 - 16:15 |
| Chair: Alba Martinez-Ruiz | Organizer: Alba Martinez-Ruiz |
| A0308: L. Pagliosa Carvalho Guedes, L. Ellen Dal Canton, M. Angel Uribe-Opazo, T. Cantu Maltauro, R. Aparecida Botinha Assumpcao | |
| Sampling redesign considering spatial $t$-Student models: An effective sample size application | |
| A0404: R. Carvajal-Schiaffino | |
| A distributed algorithm for exhaustive normality test | |
| A0307: A. Martinez-Ruiz, C. Lauro | |
| Incremental SVD for some numerical aspects of multiblock redundancy analysis and big data streams | |
| A0400: J. Acosta, R. Vallejos | |
| Assessing the estimation of nearly singular covariance matrices for modeling spatial variables | |
| A0402: A. Iodice D Enza, A. Markos, F. Palumbo | |
| Regularised PCA for incremental single imputation of missings |
| Session CO183 | Room: Aula E |
| Stochastic models for dynamical systems: Methods and computations | Wednesday 24.8.2022 14:15 - 16:15 |
| Chair: Manuel Molina | Organizer: Manuel Molina |
| A0251: S.K. Yadav, P.A.K. Laha | |
| Coalescence in branching processes with age dependent structure in population | |
| A0364: J.Y. Fan | |
| From multi-type age structure models to epidemic compartmental models | |
| A0368: M. Slavtchova-Bojkova, O. Hyrien, N. Yanev | |
| Subcritical multitype Markov branching processes with immigration generated by Poisson random measures | |
| A0317: E. Yarovaya | |
| Fundamental problems arising in the analysis of applied stochastic models | |
| A0217: M. Molina, M. Mota, A. Ramos | |
| Stochastic modeling in dynamical populations through two-sex branching processes: Inferential and computational results |
| Session CO029 | Room: Aula G |
| Dependence models | Wednesday 24.8.2022 14:15 - 16:15 |
| Chair: Fabrizio Durante | Organizer: Fabrizio Durante |
| A0177: J. Ansari, L. Ruschendorf | |
| General comparison results for factor models | |
| A0296: B. Nasri | |
| Non-central squared copulas: Properties and applications | |
| A0639: S. Guzmics | |
| Extreme value copulas based on Freund's multivariate lifetime model | |
| A0617: L. Frattarolo, R. Casarin, R. Craiu, C. Robert | |
| Living on the edge: A unified approach to antithetic sampling | |
| A0504: B. Popovic | |
| One integral transform of the copula function |
| Session CO049 | Room: Aula H |
| Optimal experimental design and applications | Wednesday 24.8.2022 14:15 - 16:15 |
| Chair: Ellinor Fackle-Fornius | Organizer: Ellinor Fackle-Fornius, Frank Miller |
| A0187: W. Mueller, R. Harman | |
| A design criterion for symmetric model discrimination based on flexible nominal sets | |
| A0291: K. Schorning, H. Dette | |
| Optimal designs for comparing regression curves: Dependence within and between groups | |
| A0373: L. Filova, R. Harman, S. Rosa | |
| Computing optimal designs of multifactor experiments | |
| A0495: F. Miller, E. Fackle-Fornius | |
| Optimal pretesting of questions for Swedish national tests in school | |
| A0511: R.E. Tsirpitzi, F. Miller | |
| Optimal dose-finding for drug combinations |
| Session CC161 | Room: Aula D |
| Statistical modelling | Wednesday 24.8.2022 14:15 - 16:15 |
| Chair: Shubhadeep Chakraborty | Organizer: COMPSTAT |
| A0195: M. Maciak | |
| Quantile LASSO with change-points in panel data models | |
| A0306: R. Miao | |
| A general joint latent class model of longitudinal and survival data with time-varying membership probability | |
| A0433: G. Martos, M. de Carvalho | |
| Uncovering regions of maximum dissimilarityon random process data | |
| A0674: A. Josang | |
| Prior weights of Dirichlet PDFs | |
| A0281: K. Huynh | |
| Weighted average least squares for negative binomial regression |
| Session CC207 | Room: Aula F |
| Spatial statistics | Wednesday 24.8.2022 14:15 - 16:15 |
| Chair: Pier Giovanni Bissiri | Organizer: COMPSTAT |
| A0248: S.J. Villejo, J. Illian, B. Swallow | |
| Data fusion in a two-stage spatio-temporal model using the INLA-SPDE approach | |
| A0524: M. Peruzzi, D. Dunson | |
| Spatial meshing and manifold preconditioning for Bayesian analysis of non-Gaussian data | |
| A0601: A. Halder | |
| Bayesian variable selection in double generalized linear Tweedie spatial process models | |
| A0660: A. Gosnell | |
| A conditional Gaussian process model for ordinal data and its application in predicting herbicidal performance | |
| A0574: F. Sigrist | |
| Latent Gaussian model boosting |
| Session CC211 | Room: Aula I |
| Mixture models | Wednesday 24.8.2022 14:15 - 16:15 |
| Chair: Christian Hennig | Organizer: COMPSTAT |
| A0276: T. Botha, J. Ferreira, A. Bekker | |
| Structured Dirichlet mixtures as priors for generalised entropy estimation | |
| A0389: S. Millard, S. Millard, F. Kanfer, M. Arashi, G. Maribe | |
| Component and feature selection in mixtures of generalised linear models | |
| A0595: A. Hairault, C. Robert, J. Rousseau | |
| Evidence estimation in finite and infinite mixture models and applications | |
| A0655: A. Sottosanti, M. Bernardi, A.R. Brazzale, A. Geringer-Sameth, D. Stenning, R. Trotta, D. van Dyk | |
| Identification of high-energy astrophysical point sources via hierarchical Bayesian nonparametric clustering | |
| A0432: T. Singh | |
| Estimation of parameters of a mixture of two exponential distributions |
| Session CC155 | Room: Aula Q |
| Semi- and nonparametric methods | Wednesday 24.8.2022 14:15 - 16:15 |
| Chair: Stefan Sperlich | Organizer: COMPSTAT |
| A0331: M. Khismatullina, M. Vogt | |
| Nonparametric comparison of epidemic time trends: The case of COVID-19 | |
| A0442: S. Girard, M. Allouche, J. El Methni | |
| A refined Weissman estimator for extreme quantiles | |
| A0491: M. Jansen | |
| Multiscale splines and local polynomials | |
| A0635: P. Willems, G. Claeskens | |
| Post-selection inference for partially linear high-dimensional single-index models | |
| A0631: T. Zhang, G. Varoquaux, J.-B. Poline, C. Greenwood | |
| Challenges in assessing lack of fit for non-parametric quantile models |
| Parallel session I: COMPSTAT2022 | Thursday 25.8.2022 | 09:00 - 11:00 |
| Session CV194 | Room: Virtual Room R1 |
| Time series (virtual) | Thursday 25.8.2022 09:00 - 11:00 |
| Chair: Francesco Violante | Organizer: COMPSTAT |
| A0416: H. Maeng, H. Cho, I. Eckley, P. Fearnhead | |
| High-dimensional time series segmentation via factor-adjusted vector autoregressive modelling | |
| A0609: Y. Zhang, Z. Liu, S. Wang | |
| Testing for the Sharpe ratio under a family of GARCH models | |
| A0630: A. Ghalayini, M. Izzeldin, M. Tsionas | |
| SHARP: A state-space HAR model with particle GIBBS sampling | |
| A0264: G. Melard | |
| On time-dependent cointegration with an application | |
| A0378: E. Iglesias | |
| Asymptotic inference for a sign-double autoregressive (SDAR) model |
| Session CO067 | Room: Aula B |
| Recent development in the network data analysis (virtual) | Thursday 25.8.2022 09:00 - 11:00 |
| Chair: Frederick Kin Hing Phoa | Organizer: Frederick Kin Hing Phoa |
| A0301: H. Jung | |
| Eliminating the biases of user influence and item popularity in bipartite networks | |
| A0327: M. Ashouri, S. Sahami, F.K.H. Phoa | |
| Forecast reconciliation using linear models: Study on time series with network structure | |
| A0363: T.-J. Yen | |
| Quality of life and multilevel contact networks: Online study among healthy adults in Taiwan | |
| A0380: W.-C. Liu | |
| Measuring uniqueness and diversity from a network perspective | |
| A0682: M.-C. Chang, F.K.H. Phoa, J.-W. Huang | |
| Designing experiments for general network structures |
| Session CO129 | Room: Aula E |
| Pioneering new frontiers in distribution and modeling | Thursday 25.8.2022 09:00 - 11:00 |
| Chair: Mohammad Arashi | Organizer: Mohammad Arashi |
| A0212: A. Bekker, M. Arashi | |
| Dirichlet distribution the superhero leading to robust innovations | |
| A0284: L. Bagnato, A. Punzo | |
| A new family of multivariate centrally symmetric distributions | |
| A0566: J. Ferreira, T. Botha, A. Bekker | |
| Practical aspects of shape mixture constructions emanating from a Dirichlet setup | |
| A0209: S.M. Salehi, A. Bekker, M. Arashi | |
| A semi-parametric density estimation | |
| A0303: O. Arslan, F.Z. Dogru | |
| Robust modeling of multivariate heterogeneous datasets using a tractable multivariate skew heavy-tailed distribution | |
| A0376: S. Kato, T. Yoshiba, S. Eguchi | |
| A copula-based measure of asymmetry between the lower and upper tail probabilities of bivariate distributions |
| Session CO168 | Room: Aula G |
| Tutorial II | Thursday 25.8.2022 09:00 - 11:00 |
| Chair: Fabrizio Durante | Organizer: COMPSTAT |
| A0613: F. Durante | |
| Tail dependence with copulas |
| Session CC150 | Room: Aula C |
| Clustering and classification | Thursday 25.8.2022 09:00 - 11:00 |
| Chair: Marta Nai Ruscone | Organizer: COMPSTAT |
| A0461: S. Bougeard, X. Bry, T. Verron, N. Niang | |
| Combined-information criterion for clusterwise elastic-net regression: Application to omic data | |
| A0619: O. Nicolis | |
| Spatio-temporal clustering and classifying of seismic events in Chile | |
| A0621: D. Han, T. Choi | |
| Bayesian high-dimensional seemingly unrelated regression model with global-local shrinkage | |
| A0643: S. Emerson, L. Aslett | |
| Joint cohort and predictive modelling | |
| A0580: Z. Taushanov, A. Berchtold, P. Ghisletta | |
| A latent Markov model approach for flexible clustering of longitudinal data |
| Session CC154 | Room: Aula D |
| Computational and financial econometrics I | Thursday 25.8.2022 09:00 - 11:00 |
| Chair: Thomas Yee | Organizer: COMPSTAT |
| A0239: K. Abduraimova | |
| Good contagion: What do networks say about policy transmission | |
| A0602: D. Xia, S. Zhu | |
| Inflation forecasts disagreement and monetary policy effectiveness | |
| A0431: H. Raubenheimer, G. Breed, T. Verster | |
| A principal component regression method to incorporate macroeconomic forecasts in modelling expected credit loss | |
| A0649: J.S. Han, N. Kordzakhia, P. Shevchenko | |
| On the state-space modelling of UK allowance futures prices | |
| A0623: Y. Croissant | |
| Count data models with endogeneity and selection |
| Session CC203 | Room: Aula F |
| Statistics and data science | Thursday 25.8.2022 09:00 - 11:00 |
| Chair: Frank Miller | Organizer: COMPSTAT |
| A0581: A. Mijanovic | |
| Distribution of a linear combination of generalized logistic random variables with application to financial returns | |
| A0593: R. Thompson, C. Forbes, S. MacEachern, M. Peruggia | |
| Familial inference | |
| A0510: G. Saporta | |
| On some issues related to the fairness of algorithms | |
| A0271: E. Siviero, E. Chautru, S. Clemencon | |
| A statistical learning view of simple kriging | |
| A0677: M. Koopmans | |
| Fractals in time series data: The methodological case for fractional differencing and power spectral density approaches |
| Session CC216 | Room: Aula H |
| Functional data analysis | Thursday 25.8.2022 09:00 - 11:00 |
| Chair: Dominik Liebl | Organizer: COMPSTAT |
| A0424: Z. Smida | |
| A Wilcoxon-Mann-Whitney spatial scan statistic for functional data | |
| A0476: Z. Hlavka, P. Coupek, V. Dolnik, D. Hlubinka | |
| Functional goodness-of-fit tests | |
| A0637: J. Lee, T. Choi | |
| Bayesian functional mixed effects model with shape constrained and hierarchical structured gaussian processes | |
| A0498: E. Fiserova, V. Rimalova, A. Menafoglio, A. Pini | |
| Permutation tests for testing hypotheses in spatial regression model with functional response | |
| A0520: T.K.H. Nguyen, M. Chiogna, D. Risso, E. Banzato | |
| Guided structure learning of DAGs for count data |
| Session CC214 | Room: Aula I |
| Robust methods II | Thursday 25.8.2022 09:00 - 11:00 |
| Chair: Christophe Croux | Organizer: COMPSTAT |
| A0500: A. Posekany | |
| Robustness and outlier detection of Bayesian model residuals with mixtures of normal, heavy-tailed and skewed components | |
| A0538: A. Toma, A. Keziou, L. Badin, S. Dedu | |
| Robust Pitman type estimators for moment condition models | |
| A0640: M. Stapper | |
| Accounting for asymmetry in M-estimation: A Julia package | |
| A0258: J. Ponnet, P. Segaert, S. Van Aelst, T. Verdonck | |
| The penalized robust double exponential estimators |
| Session CC221 | Room: Aula Q |
| Dimension reduction | Thursday 25.8.2022 09:00 - 11:00 |
| Chair: Matteo Farne | Organizer: COMPSTAT |
| A0399: J. Gibaud, X. Bry, C. Trottier | |
| Supervised component-based generalized linear regression with conditionally covarying responses | |
| A0551: S. Ishimoto, H. Minami, M. Mizuta | |
| An empirical study on nonlinear structure extraction with measures of dependence | |
| A0558: A. Freitas, M. Vichi | |
| ALS algorithm for CDPCA on high-dimensional data sets: An empirical study | |
| A0548: F. Cheng, A. Panagiotelis, R. Hyndman | |
| Anomaly detection with kernel density estimation on manifolds | |
| A0403: S. Kang, H.-S. Oh | |
| Probabilistic principal curves on Riemannian manifolds |
| Parallel session K: COMPSTAT2022 | Thursday 25.8.2022 | 14:15 - 15:45 |
| Session CV226 | Room: Aula G |
| Clustering and classification II (virtual) | Thursday 25.8.2022 14:15 - 15:45 |
| Chair: Marco Bee | Organizer: COMPSTAT |
| A0322: M. Martinez de los Santos, E. Morales-Garcia, C.O. Sosa Jimenez, M. Carmona Garcia | |
| $k$-means cluster analysis: A study on cervical cancer mortality in Veracruz, Mexico | |
| A0642: A. Giampino, R. Ascari, S. Migliorati | |
| LEFDA: An extension of the classical LDA | |
| A0668: J.-C. Lamirel, C. Hardouin | |
| Using neural clustering in spatial and non spatial models | |
| A0446: G. Perrone, G. Soffritti | |
| Parsimonious seemingly unrelated linear cluster-weighted models for contaminated data |
| Session CI005 (Special Invited Session) | Room: Aula F |
| Robust statistics | Thursday 25.8.2022 14:15 - 15:45 |
| Chair: Peter Filzmoser | Organizer: Peter Filzmoser |
| A0275: I. Wilms, G. Louvet, J. Raymaekers, G. Van Bever | |
| The influence function of graphical lasso estimators | |
| A0361: M. Salibian-Barrera, X. Ju | |
| Scalable robust estimators for non-parametric regression models | |
| A0608: C. Croux, I. Wilms, L. Bottmer | |
| Sparse regression for large data sets with outliers |
| Session CO047 | Room: Aula B |
| Statistical methods for statistically challenging data (virtual) | Thursday 25.8.2022 14:15 - 15:45 |
| Chair: Anuradha Roy | Organizer: Anuradha Roy |
| A0156: C. Drago | |
| From the analysis of the composite indicators to the analysis of the symbolic composite indicators | |
| A0279: S. Simpson, M. Bahrami, C. Tomlinson, P. Laurienti | |
| Analytical tools for whole-brain networks: Fusing statistics and network science to understand brain function | |
| A0293: W.-L. Wang | |
| Bayesian analysis of multivariate linear mixed models with censored and missing responses | |
| A0324: T.-I. Lin | |
| Model-based clustering via mixtures of unrestricted skew normal factor analyzers with missing values |
| Session CO043 | Room: Aula D |
| Association, dependence and copulas | Thursday 25.8.2022 14:15 - 15:45 |
| Chair: Sebastian Fuchs | Organizer: Sebastian Fuchs |
| A0183: F.M.L. Di Lascio, A. Menapace, M. Righetti | |
| Analysing the relationship between district heating demand and weather conditions through conditional mixture copula | |
| A0488: M. Tschimpke, S. Fuchs | |
| Total positivity of copulas from a Markov kernel perspective | |
| A0521: C. Tamborrino | |
| Statistical copulas approach for dependence in remote sensing problems | |
| A0441: T. Kasper, L. Koenig, M. Gruber, T. Soboll, W. Trutschnig | |
| Using feature selection based on multivariate statistical dependence for churn prediction in the automotive industry |
| Session CO101 | Room: Aula E |
| Econometrics methods for high dimensional data analysis | Thursday 25.8.2022 14:15 - 15:45 |
| Chair: Alessandra Amendola | Organizer: Alessandra Amendola |
| A0158: M. Caporin, D. Erdemlioglu, S. Nasini | |
| Estimating financial networks by realized interdependencies: A restricted vector autoregressive approach | |
| A0346: M.L. Parrella, F. Giordano, M. Niglio | |
| Model structure identification in spatial econometrics | |
| A0375: G. Motta, C. Baden | |
| Evolutionary correspondence analysis of the semantic dynamics of frames | |
| A0367: L. Garcia-Jorcano, M. Caporin, J.-A. Jimenez-Martin | |
| Monitoring financial stress spillovers with high-frequency principal components |
| Session CO053 | Room: Aula H |
| Non-proportional hazards in survival data | Thursday 25.8.2022 14:15 - 15:45 |
| Chair: Francesca Gasperoni | Organizer: Francesca Gasperoni |
| A0272: K. Moellenhoff, A. Tresch | |
| Survival analysis under non-proportional hazards: Investigating non-inferiority or equivalence in time-to-event data | |
| A0360: F. Ambrogi, S. Iacobelli, P.K. Andersen | |
| The differences of restricted mean survival time curves estimated using pseudo-values | |
| A0413: M. Ditzhaus, D. Dobler, A. Janssen, M. Pauly | |
| GFDsurv: A flexible toolbox to analyse nonproportional hazards in factorial survival designs | |
| A0586: J. Jimenez, D. Magirr | |
| Stratified weighted log-rank tests in settings with anticipated delayed effects |
| Session CO178 | Room: Aula Q |
| Computational statistics from the lens of young researchers I | Thursday 25.8.2022 14:15 - 15:45 |
| Chair: Marta Disegna | Organizer: Riccardo Ceccato, Marta Disegna |
| A0493: A. Gatto, F. Durante, F. Ravazzolo | |
| Dependence analysis of aggregate zonal imbalance in the Italian electricity market | |
| A0341: N. Biasetton, R. Arboretti, E. Barzizza, R. Ceccato, M. Disegna, L. Pegoraro, L. Salmaso | |
| Machine learning-based sentiment analysis with fuzzy data to predict online customer satisfaction | |
| A0349: T. Le | |
| A novel application of spatial statistics in clustering the world's diets | |
| A0423: D. Morales Navarrete, L.M. Castro, M. Bevilacqua, C. Caamano Carrillo | |
| On modelling and estimating geo-referenced count spatial data with excessive zeros |
| Session CC156 | Room: Aula C |
| High-dimensional statistics I | Thursday 25.8.2022 14:15 - 15:45 |
| Chair: Frank van der Meulen | Organizer: COMPSTAT |
| A0263: A. Chrysikou, I. Chronopoulos, G. Kapetanios | |
| High dimensional generalised penalised least squares | |
| A0507: M. Limnios, S. Clemencon, N. Vayatis | |
| The two-sample problem in high dimension: A ranking-based method | |
| A0606: A. Giessing, J. Wang | |
| Debiased inference on heterogeneous quantile treatment effects with regression rank-scores | |
| A0624: L. Wang, L. Xue | |
| Instrumental variable method in regularized regression with predictor measurement error |
| Session CC233 | Room: Aula I |
| Computational statistics and applications | Thursday 25.8.2022 14:15 - 15:45 |
| Chair: Roberto Di Mari | Organizer: COMPSTAT |
| A0687: R. Graziani, A. Compagni | |
| Time sensitive topic-based communities: The case of the vaccination debate in Italy | |
| A0689: A. Eslami, L.F. Toogood, H. Abdi | |
| Assessing similarity among groups and global components in a dual STATIS multiple correspondence analysis | |
| A0686: T. Danielius, A. Rackauskas | |
| Multiple change point detection in functional sample via G-sum process | |
| A0697: M.F. Teodoro | |
| Modeling pediatric hypertension occurrence: A case study |
| Session CP205 | Room: Virtual Posters Room II |
| Poster session II | Thursday 25.8.2022 14:15 - 15:45 |
| Chair: Cristian Gatu | Organizer: COMPSTAT |
| Parallel session L: COMPSTAT2022 | Thursday 25.8.2022 | 16:15 - 17:45 |
| Session CV196 | Room: Aula B |
| Machine learning (virtual) | Thursday 25.8.2022 16:15 - 17:45 |
| Chair: Alejandro Murua | Organizer: COMPSTAT |
| A0477: C.-E. Rabier, C. Delmas | |
| The SgenoLasso for gene mapping and genomic prediction | |
| A0512: L. Liu, S. Pal, Z. Harchaoui | |
| Large-scale entropy regularized optimal transport independence criterion | |
| A0571: M. Nguyen | |
| Actual events vs. perceived reporting: Modeling firm performance under environmental uncertainty using machine learning |
| Session CI107 (Special Invited Session) | Room: Aula F |
| Causality and distributional robustness (virtual) | Thursday 25.8.2022 16:15 - 17:45 |
| Chair: Armeen Taeb | Organizer: Peter Buehlmann |
| A0288: D. Rothenhaeusler, Y. Jeong | |
| Calibrated inference: Statistical inference that accounts for both sampling uncertainty and distributional uncertainty | |
| A0427: N. Pfister, L. Henckel, S. Saengkyongam, R. Christiansen, S. Engelke, M. Jakobsen, N. Gnecco | |
| Distribution generalization with instrumental variables | |
| A0535: A. Taeb | |
| Causal structure learning with unknown interventions |
| Session CO027 | Room: Aula C |
| Survey sampling | Thursday 25.8.2022 16:15 - 17:45 |
| Chair: Yves Tille | Organizer: Alina Matei |
| A0329: M.M. Dickson, Y. Tille, G. Espa, F. Santi, D. Giuliani | |
| A multi-spreading algorithm to account for spatial and strata heterogeneity | |
| A0516: F. Pantalone, R. Benedetti, F. Piersimoni | |
| Spbsampling: An R package for spatially balanced sampling | |
| A0651: C. Hasler, E. Eustache | |
| Model-assisted estimators in surveys with nonresponse | |
| A0393: Y. Tille | |
| Solutions inspired by survey sampling theory to build effective clinical trials |
| Session CO025 | Room: Aula D |
| New insights in robust methods of inference | Thursday 25.8.2022 16:15 - 17:45 |
| Chair: Laura Ventura | Organizer: Laura Ventura |
| A0366: V. Mameli, M. Musio, E. Ruli, L. Ventura | |
| Composite Tsallis score: A tool for robust inference | |
| A0450: C. Agostinelli, G. Saraceno | |
| Filters based on statistical data depths for robust multivariate inference | |
| A0455: M.-P. Victoria-Feser, Y. Zhang | |
| Resistant inference for complex and large models | |
| A0458: P. Grunwald | |
| E is the new P: Optional continuation and evidence |
| Session CO119 | Room: Aula E |
| Advances in k-means and clustering ensemble methods | Thursday 25.8.2022 16:15 - 17:45 |
| Chair: Roberta Pappada | Organizer: Roberta Pappada |
| A0353: A. Casa, L. Scrucca, G. Menardi | |
| Model ensemble in density-based clustering | |
| A0474: L. Egidi, R. Pappada, F. Pauli, N. Torelli | |
| Pivotal consensus clustering through the \texttt{pivmet} R package | |
| A0475: B. Pfeifer, M.G. Schimek | |
| HC-fused: A versatile R-package for multi-omics hierarchical ensemble clustering | |
| A0484: I. Melnykov | |
| $K$-means algorithm with positive and negative equivalence constraints |
| Session CO138 | Room: Aula G |
| Heavy-tailed distributions for financial modeling | Thursday 25.8.2022 16:15 - 17:45 |
| Chair: Marco Bee | Organizer: Marco Bee |
| A0250: J. Hambuckers, M. Kratz, A. Usseglio-Carleve | |
| Automatic threshold selection for extreme value regression models | |
| A0259: L. Trapin, D. Dupuis, S. Engelke | |
| Modeling panels of extremes | |
| A0313: D. Dupuis, L. Trapin | |
| Robust score-driven filters and smoothers | |
| A0347: G. Vacca, M.G. Zoia, L. Bagnato | |
| Forecasting in GARCH models with polynomially modified innovations |
| Session CO136 | Room: Aula I |
| Recent developments in high-dimensional statistics | Thursday 25.8.2022 16:15 - 17:45 |
| Chair: Shubhadeep Chakraborty | Organizer: Shubhadeep Chakraborty |
| A0339: R. Ceccato, R. Arboretti, E. Barzizza, N. Biasetton, M. Disegna, L. Pegoraro, L. Salmaso | |
| A multivariate permutation test for the analysis of paired samples: the mixed data scenario | |
| A0486: S. Chakraborty | |
| Nonparametric sequential change-point detection in high dimensions | |
| A0362: D. Nandy, D. Ghosh, K. Kechris | |
| DisCo P-ad: Distance Correlation-based P-value Adjustment boosts multiple-testing corrections in metabolomics analyses | |
| A0267: Y. Chen, D. Witten | |
| Selective inference for k-means clustering |
| Session CO055 | Room: Aula Q |
| Biostatistics and biocomputing | Thursday 25.8.2022 16:15 - 17:45 |
| Chair: Yisheng Li | Organizer: Yisheng Li |
| A0438: C.-H. Hsu, Y. He, C. Hu, W. Zhou | |
| A multiple imputation-based sensitivity analysis approach for regression analysis with a MNAR covariate | |
| A0616: Y. Li | |
| A uniform shrinkage prior in spatio-temporal Poisson models for count data | |
| A0644: S. Zhou | |
| Posterior predictive design for phase I clinical trials | |
| A0665: M. Guindani | |
| Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data |
| Session CC135 | Room: Aula H |
| Multivariate data analysis I | Thursday 25.8.2022 16:15 - 17:45 |
| Chair: Friedrich Leisch | Organizer: COMPSTAT |
| A0546: S. Bonnini, M. Borghesi | |
| A combined permutation test for comparing marginal probabilities of multivariate binary variables | |
| A0395: J. Kalina | |
| Testing exchangeability of multivariate distributions | |
| A0568: A. Francisco, P. Monteiro, G. Ribeiro, A.S. Teixeira | |
| On edge significance in large phylogenetic (spanning) trees | |
| A0569: C. Vaz, A. Francisco | |
| Towards the optimization of large-scale phylogenetic trees |
| Parallel session M: COMPSTAT2022 | Friday 26.8.2022 | 09:00 - 10:30 |
| Session CV190 | Room: Aula H |
| Computational and financial econometrics III | Friday 26.8.2022 09:00 - 10:30 |
| Chair: Alessandra Amendola | Organizer: COMPSTAT |
| A0648: J.-B. Hasse, B. Candelon | |
| The effect of climate policies on carbon emissions reduction | |
| A0241: B. Li, Z. Liu, S. Wang, Y. Zhang | |
| Asset Pricing Model with Functional Principal Component Analysis | |
| A0266: I. Chronopoulos, L. Giraitis, G. Kapetanios | |
| Choosing between persistent and stationary volatility | |
| A0612: J. Vega Baquero, M. Santolino | |
| Capital flows in integrated capital markets: The MILA case |
| Session CV227 | Room: Aula I |
| Regression models | Friday 26.8.2022 09:00 - 10:30 |
| Chair: Peter Grunwald | Organizer: COMPSTAT |
| A0633: J. Xu, T. Zou, A. Wood, J. Scealy | |
| Generalized score matching for regression | |
| A0622: K. Wada, T. Kurosawa | |
| An approximation of the corrected naive estimator for a Poisson regression model with a measurement error | |
| A0532: S. Renzetti, C. Gennings, S. Calza | |
| A weighted quantile sum regression with penalized weights and two indices | |
| A0407: S. Chandrasena, R. Liu | |
| Divide and conquer approaches for nonparametric regression and variable selection |
| Session CO037 | Room: Aula C |
| Clustering methods and copula function | Friday 26.8.2022 09:00 - 10:30 |
| Chair: F Marta L Di Lascio | Organizer: F Marta L Di Lascio |
| A0508: R. Pappada, F. Durante, S. Fuchs | |
| Copula-based clustering of dependent variables with application to flood risks | |
| A0392: M. Nai Ruscone, A. Dambrosio, D. Fernandez | |
| Copula-based non-metric unfolding | |
| A0218: G. Malsiner-Walli, S. Fruhwirth-Schnatter, B. Gruen | |
| Mixtures with a prior on the number of components and the telescoping sampler | |
| A0425: F. Condino, A. Irpino, R. Verde | |
| Clustering Italian regions on the basis of bivariate income and consumption distributions |
| Session CO148 | Room: Aula D |
| Early career advice for statisticians in the computational sciences | Friday 26.8.2022 09:00 - 10:30 |
| Chair: Thomas Yee | Organizer: Thomas Yee |
| Session CO035 | Room: Aula F |
| Recent developments of variational approximations | Friday 26.8.2022 09:00 - 10:30 |
| Chair: Mauro Bernardi | Organizer: Mauro Bernardi |
| A0464: N. Bianco, M. Bernardi | |
| Variational Bayes for dynamic sparsity in time varying parameter regression with many predictors | |
| A0481: C. Castiglione, M. Bernardi | |
| Bayesian non-conjugate regression via variational belief updating | |
| A0513: L. Maestrini, E. Degani, D. Toczydlowska, M.P. Wand | |
| Streamlined variational inference for sparse linear mixed model selection | |
| A0629: M. Cattelan, M. Bernardi, C. Busatto | |
| Fast Bayesian model selection algorithms for linear regression models |
| Session CO051 | Room: Aula G |
| IASC-ARS session: Computations for categorical data (virtual) | Friday 26.8.2022 09:00 - 10:30 |
| Chair: Yuichi Mori | Organizer: Yuichi Mori |
| A0292: R. Lombardo, E. Beh | |
| The Cressie-Read divergence statistic and correspondence analysis; a unifying approach with possible extensions | |
| A0369: J. Nakano, N. Shimizu, Y. Yamamoto | |
| A multiple correspondence analysis for aggregated symbolic data | |
| A0489: M. van de Velden, A. Iodice D Enza, A. Markos, C. Cavicchia | |
| A general framework for implementing distance measures for categorical variables | |
| A0544: C.-H. Chen, S.-A. Chen, C.-H. Kao, S.-H. Shieh, H.-M. Wu | |
| cGAPdb: A matrix visualization database for categorical data sets |
| A0460: H. Hamada, K. Honda | |
| Development of visualizing system based on research networked data for open science age | |
| A0286: F.K.H. Phoa, H. Jung, M. Ashouri | |
| A leading author model for the popularity effect on scientific collaboration | |
| A0355: Y. Mizukami, J. Nakano | |
| Assessing the research strength of organizations focusing on intrapersonal diversity in applied research of AI | |
| A0705: N. Zakiyeva, Y. Chen, T. Koch, K. Liu, Z. Xu, C.-H. Chen, J. Nakano, K. Honda | |
| Article's Scientific Prestige: measuring the impact of individual articles in the Web of Science |
| Session CC231 | Room: Aula B |
| Time series and financial econometrics | Friday 26.8.2022 09:00 - 10:30 |
| Chair: Massimiliano Caporin | Organizer: COMPSTAT |
| A0692: A. Marcocchia, S. Arima, P. Brutti | |
| A convolutional approach to forecast reconciliation | |
| A0694: J. Kukacka, L. Kristoufek | |
| Fundamental and speculative components of the cryptocurrency pricing dynamics | |
| A0683: P. Chen | |
| Instability in SETAR models | |
| A0700: M. Takahashi, Y. Omori, T. Watanabe, Y. Yamauchi | |
| Realized stochastic volatility models with skew-t distributions |
| Session CC228 | Room: Aula E |
| Multivariate data analysis II | Friday 26.8.2022 09:00 - 10:30 |
| Chair: Jan Bauer | Organizer: COMPSTAT |
| A0671: M. AL-Shukeili, R. Wesonga | |
| Transformation and covariance estimation for the non-linearly separable misclassification problem | |
| A0387: C. Muehlmann, S. De Iaco, K. Nordhausen | |
| Blind source separation for multivariate stationary space-time data | |
| A0333: J. Bauer | |
| Sparse principal loading analysis | |
| A0463: C. Rieser | |
| Connecting compositional data to graph signal processing |
| Session CC210 | Room: Aula Q |
| Graphical models and networks | Friday 26.8.2022 09:00 - 10:30 |
| Chair: Mohammad Arashi | Organizer: COMPSTAT |
| A0529: E. Nezakati Rezazadeh, E. Pircalabelu | |
| Estimation and inference for covariate-adjusted Gaussian graphical models via an unbalanced distributed setting | |
| A0627: A. Freni Sterrantino, D. rustand, H. Rue | |
| A catalogue of graph-based multivariate conditional autoregressive model | |
| A0246: M. Signorelli | |
| Analysing populations of networks with mixtures of generalized linear mixed models | |
| A0523: M. Gregorich | |
| Flexible parametrization of graph-theoretical features from individual-specific networks for prediction |
| Parallel session N: COMPSTAT2022 | Friday 26.8.2022 | 11:00 - 12:00 |
| Session CV199 | Room: Aula C |
| Multivariate data analysis (virtual) | Friday 26.8.2022 11:00 - 12:00 |
| Chair: Sonja Greven | Organizer: COMPSTAT |
| A0256: N. Yamashita | |
| Two-stage target rotation with computational efficiency by asymmetric least squares criterion | |
| A0565: J. Nienkemper-Swanepoel, N. Le Roux, S. Lubbe | |
| Sensitivity analysis of the choice of multiple imputation approach on categorical GPAbin biplots | |
| A0550: T.-W. Wang, E. Beh | |
| Power transformation of reciprocal averaging |
| Session CV202 | Room: Aula G |
| Survival analysis (virtual) | Friday 26.8.2022 11:00 - 12:00 |
| Chair: Martina Mittlboeck | Organizer: COMPSTAT |
| A0408: S. Agami | |
| Estimation in the Cox survival regression model with covariate measurement error and a changepoint | |
| A0410: Y. Nakagawa, T. Sozu | |
| Improvement of midpoint imputation for estimation of median survival time for interval-censored time-to-event data | |
| A0656: J. Goungounga, O. Boussari, V. Jooste | |
| Simulating survival data for cure models in overall or net survival framework |
| Session CV186 | Room: Aula I |
| Clustering and classification I (virtual) | Friday 26.8.2022 11:00 - 12:00 |
| Chair: Tsung-I Lin | Organizer: COMPSTAT |
| A0543: M. Sato-Ilic | |
| Fuzzy cluster-scaled principal component analysis for high-dimension low-sample data | |
| A0547: N. Raveendran, G. Sofronov | |
| A hybrid cross entropy method for spatial clustering problems | |
| A0555: K. Takahashi, K. Yamamoto | |
| Confidence interval for recall and precision of multi-class classification |
| Session CI099 (Special Invited Session) | Room: Aula F |
| Data visualization and model selection | Friday 26.8.2022 11:00 - 12:00 |
| Chair: Christophe Croux | Organizer: Christophe Croux |
| A0673: C. Hurley | |
| Data and model visualisation for statistical learning problems | |
| A0645: A. Merida, A. Kalogeratos, M. Mougeot | |
| Data inspection via challenging decision boundaries' rigidity |
| Session CO075 | Room: Aula D |
| Computational statistics for applications | Friday 26.8.2022 11:00 - 12:00 |
| Chair: Marta Disegna | Organizer: Marta Disegna |
| A0285: B. Gruen, H. Wagner, T. Petzoldt | |
| Estimating the susceptible component of a zone diameter distribution | |
| A0340: M. Disegna, R. Ceccato, R. Arboretti, E. Barzizza, N. Biasetton, L. Pegoraro, L. Salmaso | |
| A multivariate permutation test for the analysis of market research data | |
| A0527: L. Bocci, P. Durso, V. Vitale | |
| INDCLUS for spatial proximity data |
| Session CO174 | Room: Aula H |
| Geostatistics | Friday 26.8.2022 11:00 - 12:00 |
| Chair: Pier Giovanni Bissiri | Organizer: Pier Giovanni Bissiri |
| A0326: C. Gaetan, P. Bortot | |
| A model for space-time threshold exceedances | |
| A0357: R. Furrer, M. Hediger | |
| Asymptotic properties of pseudo-ML estimators based on covariance approximations | |
| A0675: P.G. Bissiri, E. Porcu, F. Tangle, R. Soza, F. Quintana | |
| Positive definite functions on spheres: Some statistical and mathematical issues |
| A0467: L.V. Ballestra, E. DInnocenzo, A. Guizzardi | |
| Pricing options using a score-driven model with jumps | |
| A0672: B. Bertelli, C. Torricelli | |
| ESG dimensions in screening strategies: Impact on portfolio performance in periods of financial distress | |
| A0300: M. Kanno | |
| Exploring risk hidden in syndicated loan networks: Evidence from real estate investment trusts |
| Session CC232 | Room: Aula B |
| High-dimensional statistics and model assesment | Friday 26.8.2022 11:00 - 12:00 |
| Chair: Germain Van Bever | Organizer: COMPSTAT |
| A0701: Y. Zhou, K. Yuen | |
| Estimation in the high dimensional additive hazard model with l0 type of penalty | |
| A0693: L. Li, M. Gupta, V. Macaulay | |
| Bayesian group Lasso regression for genome-wide association studies | |
| A0703: V.S. Barbu, T. Gkelsinis, A. Karagrigoriou | |
| Statistical inference based on weighted divergence measures |
| Session CC224 | Room: Aula E |
| Longitudinal data | Friday 26.8.2022 11:00 - 12:00 |
| Chair: Silvia Pandolfi | Organizer: COMPSTAT |
| A0257: Z. Oflaz | |
| Estimation of disease progression for ischemic heart disease using latent Markov with covariates | |
| A0457: P. Arsenteva, V. Paget, O. Guipaud, F. Milliat, H. Cardot, M.A. Benadjaoud | |
| Clustering with alignment and network inference to study the radiation response of endothelial cells | |
| A0559: M. Moazeni | |
| A personalized remote patient monitoring system using daily measurements of bodyweight, heart rate, and blood pressure |