KEYNOTE TALKS (UTC+1)
| Keynote talk I | Tuesday 22.8.2023 | 09:00 - 10:00 | Room: CLO B01 |
| Multi-objective optimisation of restricted randomised designs | |||
| Speaker: K. Mylona | Chair: Maria Brigida Ferraro | ||
| Keynote talk II | Tuesday 22.8.2023 | 18:00 - 18:55 | Room: CLO B01 |
| The historical role of energy in UK inflation and productivity | |||
| Speaker: D. Hendry | Chair: Francisco Javier Rubio | ||
| Keynote talk III | Friday 25.8.2023 | 12:10 - 13:10 | Room: BCB 307 |
| Subject prevalence in documents based on topic modeling | |||
| Speaker: A. Colubi Co-authors: L. Kontoghiorghes | Chair: David Weston | ||
PARALLEL SESSIONS (UTC+1)
| Parallel session B: COMPSTAT2023 | Tuesday 22.8.2023 | 10:30 - 12:30 |
| Session CO015 | Room: BCB 311 |
| Categorical and high-dimensional data analysis | Tuesday 22.8.2023 10:30 - 12:30 |
| Chair: Mark De Rooij | Organizer: Mark De Rooij, Rosaria Lombardo |
| A0216: J. Nienkemper-Swanepoel, N. Le Roux, S. Lubbe | |
| The impact of (un)congenial multiple imputation approaches on GPAbin biplots | |
| A0230: M. Gallo | |
| A strategy for improving the speed in tensor decomposition analysis | |
| A0244: T.-W. Wang, E. Beh, R. Lombardo | |
| Assessing dispersion in a two-way contingency table under profile transformations and reciprocal averaging | |
| A0253: M. De Rooij | |
| Logistic multidimensional data analysis for ordinal response variables using a cumulative link function | |
| A0287: A. Iodice D Enza, M. van de Velden, C. Cavicchia, A. Markos | |
| Association-based distances for categorical and mixed-type data |
| Session CO112 | Room: Virtual room R01 |
| Tutorial I | Tuesday 22.8.2023 10:30 - 12:30 |
| Chair: Alessandra Luati | Organizer: COMPSTAT |
| A0179: A. Luati | |
| Dynamic models for multiple quantiles |
| Session CC073 | Room: BCB 307 |
| Spatial statistics | Tuesday 22.8.2023 10:30 - 12:30 |
| Chair: Klaus Nordhausen | Organizer: COMPSTAT |
| Session CC062 | Room: BCB 308 |
| Biostatistics | Tuesday 22.8.2023 10:30 - 12:30 |
| Chair: Stefan Van Aelst | Organizer: COMPSTAT |
| A0326: M. Misumi, H. Sugiyama | |
| A multi-state modeling with Poisson regression utilizing grouped data in a radiation epidemiological study | |
| A0379: K. Le Gall, L. Bellanger, A. Stamm, D. Laplaud | |
| Generation of synthetic mixed data for multiple sclerosis patients: Application to gait data and EDSS score | |
| A0340: H. Kobayashi, M. Okabe, H. Yadohisa | |
| Dimensionality reduction for multi-omics data using the Freeman-Tukey transformation | |
| A0319: P. Arsenteva, M.A. Benadjaoud, H. Cardot | |
| Estimating the linear relation between variables that are never jointly observed: An application to in vivo experiments | |
| A0325: R. Coletti, M. Lopes, S. Martins | |
| Network inference and robust clustering on high-dimensional data to investigate molecular heterogeneity in glioma |
| Session CC030 | Room: BCB 310 |
| Time series | Tuesday 22.8.2023 10:30 - 12:30 |
| Chair: Matus Maciak | Organizer: COMPSTAT |
| A0185: M.T. Kurbucz, A. Jakovac, P. Posfay | |
| Linear law-based feature space transformation | |
| A0186: W.-Y. Wu | |
| Regularized nonlinear regression with dependent errors and its application to a biomechanical model | |
| A0364: N. Zakiyeva | |
| High-dimensional high-frequency time series prediction model solved with a mixed integer optimisation method | |
| A0386: V. Pastukhov | |
| Fused lasso nearly-isotonic signal approximation in general dimensions | |
| A0389: M. Kilinc, M. Massmann | |
| The modified conditional sum-of-squares estimator for fractionally integrated models |
| Parallel session C: COMPSTAT2023 | Tuesday 22.8.2023 | 14:15 - 15:45 |
| Session CO016 | Room: BCB 307 |
| Bayesian methods | Tuesday 22.8.2023 14:15 - 15:45 |
| Chair: TBA | Organizer: Cathy W-S Chen |
| A0275: , J. Nakajima | |
| Time-varying parameter heterogeneous autoregressive model with stochastic volatility | |
| A0290: F.C. Liu, C.W.-S. Chen, H.-H. Hsu | |
| Bayesian model selection among dispersed integer-valued time series models | |
| A0291: T.-H. Fan, Y.-S. Dong, C.-Y. Peng | |
| A complete Bayesian degradation analysis based on inverse Gaussian processes | |
| A0315: C.-H. Weng | |
| Rating of players by Laplace approximation and dynamic modeling |
| Session CO028 | Room: BCB 310 |
| Rank-based inference, feature selection, and data consolidation | Tuesday 22.8.2023 14:15 - 15:45 |
| Chair: Valeria Vitelli | Organizer: Michael Georg Schimek |
| A0195: P. Yu, Y. Zhuang | |
| Modeling of preference data with multiple network views | |
| A0256: V. Vitelli | |
| Rank-based Bayesian joint variable selection and clustering of genome-wide transcriptomic data | |
| A0237: M. La Rocca, B. Pfeifer, M.G. Schimek | |
| Bootstrap inference for signal reconstruction from multiple ranked lists |
| Session CO026 | Room: BCB 311 |
| CMStatistics session: Statistical analysis of complex data | Tuesday 22.8.2023 14:15 - 15:45 |
| Chair: Enea Bongiorno | Organizer: Xuming He |
| A0155: L. Li | |
| Kernel ordinary differential equations | |
| A0161: P. Nag | |
| Spatio-temporal deepkriging for interpolation and probabilistic forecasting | |
| A0227: M. Karemera, S. Guerrier, S. Orso, M.-P. Victoria-Feser, Y. Zhang, Y. Ma | |
| A flexible bias correction method based on inconsistent estimators | |
| A0239: E. Bura, A. Kofnov, E. Bartocci, M. Moosbrugger, M. Stankovic | |
| Exact and approximate moment derivation for probabilistic loops with non-polynomial assignments |
| Session CO017 | Room: Virtual room R01 |
| Statistical methods for spatial and spatio-temporal data | Tuesday 22.8.2023 14:15 - 15:45 |
| Chair: Hsin-Cheng Huang | Organizer: Hsin-Cheng Huang |
| A0165: J. Zhu | |
| Scalable semiparametric spatio-temporal regression for large data analysis | |
| A0215: J. Yang | |
| On minimum contrast method for multivariate spatial point processes | |
| A0359: C.-S. Chen, C.-W. Shen | |
| ZIP-like models for spatial count processes | |
| A0269: H.-C. Huang | |
| Nonstationary spatial modeling, estimation, and prediction using a divide-and-conquer approach |
| Session CC053 | Room: BCB 308 |
| Statistical modelling and inference | Tuesday 22.8.2023 14:15 - 15:45 |
| Chair: Mark De Rooij | Organizer: COMPSTAT |
| A0158: A. Muhammad, S. Ahmad | |
| A new proposal to mitigate multicollinearity in linear regression models | |
| A0173: S. Ferreira, D. Ferreira | |
| Unbiased estimators of the cumulants under bi-additive models | |
| A0301: S. Guenay | |
| Analysis of parameter and partial parameter impacts | |
| A0328: Y. Iguchi, A. Beskos, M. Graham | |
| Parameter estimation with increased precision for elliptic and hypo-elliptic diffusions |
| Parallel session D: COMPSTAT2023 | Tuesday 22.8.2023 | 16:15 - 17:45 |
| Session CI003 (Special Invited Session) | Room: BCB 307 |
| Bayesian nonparametric methods and computing | Tuesday 22.8.2023 16:15 - 17:45 |
| Chair: Michele Guindani | Organizer: Michele Guindani |
| A0400: L. Dalla Valle, R. Barone | |
| Bayesian nonparametric inference for conditional vine copulas | |
| A0401: F. Camerlenghi, A. Colombi, L. Paci, R. Argiento | |
| Mixture modeling via vectors of normalized independent finite point processes | |
| A0408: Y. Raykov | |
| Adaptive latent feature sharing for piecewise lineardimensionality reduction |
| Session CO027 | Room: BCB 309 |
| Recent advances in statistical learning | Tuesday 22.8.2023 16:15 - 17:45 |
| Chair: Thierry Chekouo | Organizer: Alejandro Murua |
| A0193: D. Larocque, C. Alakus, A. Labbe | |
| Covariance regression with random forests | |
| A0248: S. Vicente | |
| Clustering with diversity: A promising approach with the determinantal point process | |
| A0266: A. Ali, A. Bhullar, K. Nadeem | |
| Multi-crop land suitability prediction from remote sensing data using semi-supervised learning | |
| A0247: T. Chekouo | |
| A Bayesian variable selection approach incorporating prior feature ordering and population structures |
| Session CO019 | Room: BCB 310 |
| Data depth: A focus on computation and anomaly detection | Tuesday 22.8.2023 16:15 - 17:45 |
| Chair: Pavlo Mozharovskyi | Organizer: Pavlo Mozharovskyi |
| A0258: S. Hopkins | |
| Mean estimation, differential privacy, and the sum of squares method | |
| A0231: R. Dyckerhoff, S. Nagy | |
| An efficient algorithm for computing the angular halfspace depth of a whole sample | |
| A0252: S. Nagy | |
| Exact computation of the scatter halfspace depth | |
| A0251: G. Staerman | |
| Affine-invariant integrated rank-weighted depth: Definition, properties and finite sample analysis |
| Session CC086 | Room: BCB 308 |
| Multivariate statistics | Tuesday 22.8.2023 16:15 - 17:45 |
| Chair: Ray-Bing Chen | Organizer: COMPSTAT |
| A0188: P.O. Obanya, R. Coetzer, C. Olivier, T. Verster | |
| Variable contribution analysis in multivariate process monitoring using permutation entropy | |
| A0172: D. Ferreira, S. Ferreira | |
| Optimizing allocation rules in discrete and continuous discriminant analysis | |
| A0320: T.-L. Chen | |
| Variable selection via information gain | |
| A0365: R. Motegi, Y. Seki | |
| Variable discretization-based screening for high dimensional data |
| Session CC110 | Room: BCB 311 |
| Time series in applications | Tuesday 22.8.2023 16:15 - 17:45 |
| Chair: Niklas Ahlgren | Organizer: COMPSTAT |
| A0330: A. Hanebeck, C. Czado | |
| Multivariate analysis of mortality data using time-varying copula state space models | |
| A0347: S. Tavares, L. Krippahl, M. Lopes | |
| Feature extraction from satellite data for multivariate time-series forecasting of biotoxin contamination in shellfish | |
| A0377: C.S. Santos, I. Pereira | |
| A periodic integer-valued time series with an application to fire activity | |
| A0393: V. Mendes, D. Mendes | |
| Nonlinear factor analysis for large sets of macroeconomic time series |
| Parallel session F: COMPSTAT2023 | Wednesday 23.8.2023 | 09:00 - 10:30 |
| Session CI006 (Special Invited Session) | Room: Virtual room R01 |
| Modern statistical analysis for dependent data | Wednesday 23.8.2023 09:00 - 10:30 |
| Chair: Mike So | Organizer: Mike So |
| A0181: R. Ganey | |
| High-dimensional LDA biplot through the GSVD | |
| A0234: P. Menendez, M.J. Barcena, M.C. Gonzalez, F. Tusell | |
| Have house prices factored in the risks of climate change? | |
| A0242: R. Lombardo, E. Beh, A. Ceriello, G. Lucisano, F. Prattichizzo, A. Nicolucci | |
| Visualizing departures from symmetry: A study on cardiovascular risk among patients with diabetes | |
| A0310: M. Mayrhofer, P. Filzmoser | |
| Explainable outlier detection based on Shapley values |
| Session CC046 | Room: BCB 309 |
| Machine learning | Wednesday 23.8.2023 09:00 - 10:30 |
| Chair: Peter Winker | Organizer: COMPSTAT |
| A0204: G.-H. Huang | |
| Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction | |
| A0217: M. Waltz, O. Okhrin | |
| Two-sample testing in reinforcement learning | |
| A0361: C.J. Perez Sanchez, L. Naranjo, V. Miranda, J. Hernandez | |
| Generalized additive models for multiclass detection of voice disorders by using acoustic features | |
| A0369: C. Lehner | |
| On the ability of random forests to model interactions |
| Session CC033 | Room: BCB 310 |
| Algorithms and computational methods | Wednesday 23.8.2023 09:00 - 10:30 |
| Chair: Dominik Liebl | Organizer: COMPSTAT |
| A0296: R. Valla, P. Mozharovskyi, F. d Alche-Buc | |
| Anomaly component analysis: Visualization and interpretability for anomaly detection | |
| A0298: S. Dominique, V. Cariou, M. Hanafi, J.-M. Ferrandi, F. Llobell | |
| A simple and direct procedure for data generation in PLS-SEM framework | |
| A0302: H.-M. Wu | |
| dataSDA and ggESDA: Two R packages for exploratory symbolic data analysis |
| Session CC057 | Room: BCB 311 |
| Semi- and nonparametric methods | Wednesday 23.8.2023 09:00 - 10:30 |
| Chair: Ivan Kojadinovic | Organizer: COMPSTAT |
| A0321: M. Kitani, K. Yuasa, H. Murakami | |
| Prediction of order statistics based on ordered generalized ranked set sampling | |
| A0334: G.-N. Brunotte | |
| A measure for the degree of distribution changes in locally stationary processes | |
| A0372: S. Zhu, A. Celisse | |
| Bandwidth selection method for estimating difference between two densities with kernel density estimation | |
| A0207: I. Kojadinovic, M. Holmes, A. Verhoijsen | |
| Open-end monitoring for multivariate observations sensitive to all types of changes in the distribution function |
| Parallel session G: COMPSTAT2023 | Wednesday 23.8.2023 | 11:00 - 12:30 |
| Session CI004 (Special Invited Session) | Room: BCB 307 |
| Change-point analysis | Wednesday 23.8.2023 11:00 - 12:30 |
| Chair: Ivan Kojadinovic | Organizer: Ivan Kojadinovic |
| A0156: C. Kirch, H. Cho | |
| Data segmentation: Moving-sum-procedures and bootstrap confidence intervals | |
| A0199: T. Wang | |
| Sparse change detection in high-dimensional linear regression | |
| A0240: G. Rice, A. Aue, L. Horvath, Y. Zhao, J. Vander Does, O. Sonmez | |
| Change point analysis of functional time series |
| Session CO008 | Room: BCB 309 |
| Causal inference and functional data analysis | Wednesday 23.8.2023 11:00 - 12:30 |
| Chair: Nicolas Hernandez | Organizer: Nicolas Hernandez |
| A0191: D. Liebl, T. Mensinger | |
| Causal inference with functional data | |
| A0224: K. Ecker, X. de Luna, L. Schelin | |
| Causal inference with a functional outcome | |
| A0226: E. Solea | |
| High-dimensional nonparametric functional graphical models via the functional additive partial correlation operator | |
| A0263: S. Greven, L. Steyer, A. Stoecker | |
| Elastic linear regression for curves in $R^d$ |
| Session CO025 | Room: BCB 310 |
| HiTEc session: Advances in statistics for finance | Wednesday 23.8.2023 11:00 - 12:30 |
| Chair: Massimiliano Caporin | Organizer: Alessandra Amendola, Massimiliano Caporin |
| A0286: G. Bonaccolto, R. Riccobello, P.J. Kremer, S. Paterlini, M. Bogdan | |
| Sparse graphical modelling for minimum variance portfolios | |
| A0223: B. Sanhaji | |
| Nonlinear scalar BEKK | |
| A0225: M. Puke, T. Dimitriadis | |
| Forecast calibration, backtests, and score decompositions for Value-at-Risk | |
| A0174: M. Caporin, G. Storti | |
| Penalized CAW, forecast error variance decompositions and systemic risk measurement |
| Session CO100 | Room: Virtual room R01 |
| Clustering and regression analysis of complex real-life data | Wednesday 23.8.2023 11:00 - 12:30 |
| Chair: Gabriele Soffritti | Organizer: Gabriele Soffritti |
| A0218: S.D. Tomarchio, A. Punzo, L. Bagnato | |
| Mixture models for heavy-tailed tensor-variate data | |
| A0261: V. Vandewalle, M. du Roy de Chaumaray | |
| Non-parametric multi-partitions clustering | |
| A0272: G. Babu | |
| Model based labelling of hyperspectral food images | |
| A0259: G. Soffritti, G. Perrone | |
| Mixtures of linear regression models: An application to housing tension in Emilia-Romagna, Italy |
| Session CC082 | Room: BCB 311 |
| High-dimensional statistics | Wednesday 23.8.2023 11:00 - 12:30 |
| Chair: Maria Brigida Ferraro | Organizer: COMPSTAT |
| A0341: N. Makigusa | |
| Two-sample test based on the variance of a positive definite kernel | |
| A0208: N. Chakraborty, C.F. Lui, M. Ahmed | |
| A distribution-free change-point monitoring scheme in high-dimensional settings | |
| A0370: H. Choi, Q. Mai | |
| Skew-normal classification in high-dimensional data | |
| A0333: H. Kwon, Y. Liao, J. Choi | |
| Inference for low-rank models without rank estimation |
| Parallel session H: COMPSTAT2023 | Wednesday 23.8.2023 | 14:15 - 15:45 |
| Session CV072 | Room: Virtual room R01 |
| Spatial statistics | Wednesday 23.8.2023 14:15 - 15:45 |
| Chair: Ivan Kojadinovic | Organizer: COMPSTAT |
| A0329: M. Hediger, R. Furrer | |
| Asymptotic analysis of ML-covariance parameter estimators based on covariance approximations | |
| A0346: Z. Quiroz, M. Prates, D. Dey, H. Rue | |
| Fast Bayesian inference of block nearest neighbor Gaussian models for large data | |
| A0171: S. Kim, M. Kaiser, X. Dai | |
| A generalized functional linear model with spatial dependence |
| Session CO104 | Room: BCB 308 |
| Statistics for data science | Wednesday 23.8.2023 14:15 - 15:45 |
| Chair: Luis Alberto Firinguetti Limone | Organizer: Luis Alberto Firinguetti Limone |
| A0166: D.F. Munoz | |
| Estimation of expectations in two-level nested simulation experiments | |
| A0178: P. Canas Rodrigues | |
| Bayesian spatio-temporal modeling of the Brazilian wildfires: The influence of human and meteorological variables | |
| A0197: M. Bohorquez | |
| Building and classifying brain images | |
| A0200: L.A. Firinguetti Limone, L. Gomez | |
| Shrinkage estimators for beta regression models |
| Session CO107 | Room: BCB 309 |
| Advances in multi-view learning and mixture models | Wednesday 23.8.2023 14:15 - 15:45 |
| Chair: Angela Montanari | Organizer: Angela Montanari |
| A0241: S. Dallari, L. Anderlucci, A. Montanari | |
| Finding groups in microbiome data according to multiple data-views | |
| A0288: K. Van Deun | |
| Finding the hidden link: Statistical methods for multi-view high-dimensional data | |
| A0167: O. Laverny, P. Lambert | |
| Local moment matching with Gamma mixtures under automatic smoothness penalization | |
| A0327: P. Duttilo, S.A. Gattone, A. Kume | |
| Mixtures of generalised normal distribution with constraints |
| Session CO103 | Room: BCB 310 |
| Dynamic networks | Wednesday 23.8.2023 14:15 - 15:45 |
| Chair: Philip Yu | Organizer: Philip Yu |
| A0159: J. Gu, G. Yin | |
| Triangular concordance learning of networks | |
| A0189: B. Jiang | |
| A two-way heterogeneity model for dynamic networks | |
| A0284: G. Li | |
| High-dimensional low-rank linear time series modeling | |
| A0358: V. Batagelj | |
| Analysis of weighted temporal networks represented by time slices |
| Session CO101 | Room: BCB 311 |
| Novel perspectives in Bayesian statistics | Wednesday 23.8.2023 14:15 - 15:45 |
| Chair: Gavino Puggioni | Organizer: Pier Giovanni Bissiri |
| A0198: P. White, P.G. Bissiri, E. Porcu, G. Cleanthous, A. Alegria | |
| Multivariate isotropic random fields on spheres: Nonparametric Bayesian modeling and $L_p$ fast approximations | |
| A0202: D. Frazier | |
| Reliable Bayesian inference in misspecified models | |
| A0265: G. Puggioni, M. Cannas | |
| On the Voigt distribution: Characterization and parameter estimation | |
| A0350: D. Christensen, P.A. Moen | |
| Fast implementation of a general importance sampling algorithm for Bayesian nonparametric models with binary responses |
| Parallel session I: COMPSTAT2023 | Thursday 24.8.2023 | 09:00 - 10:00 |
| Session CC114 | Room: BCB 307 |
| Generalized linear models | Thursday 24.8.2023 09:00 - 10:00 |
| Chair: Sara Taskinen | Organizer: COMPSTAT |
| A0345: V. Asimit, A. Badescu, F. Zhou | |
| Efficient and proper GLM modelling with power link functions | |
| A0362: C. Kleiber, S. Acemoglu, J. Urban | |
| Variable importance in generalized linear models: A unifying view using Shapley values | |
| A0378: L. Maestrini, F. Hui, A. Welsh | |
| Restricted maximum likelihood estimation in generalized linear mixed models |
| Session CC061 | Room: BCB 308 |
| Design of experiments | Thursday 24.8.2023 09:00 - 10:00 |
| Chair: Peter Winker | Organizer: COMPSTAT |
| A0311: S. Gilmour, P.-W. Tsai | |
| Optimal two-level designs under model uncertainty | |
| A0351: S. Alzahrani | |
| Nonlinear models for mixture experiments including process variables | |
| A0390: E. Fackle-Fornius, F. Miller | |
| Efficient calibration of items in mixed format achievement tests using optimal design methodology |
| Session CC109 | Room: BCB 309 |
| Time series econometrics | Thursday 24.8.2023 09:00 - 10:00 |
| Chair: Davide La Vecchia | Organizer: COMPSTAT |
| A0162: D. Buncic | |
| On a standard method for measuring the natural rate of interest | |
| A0169: C.-A. Liu, T.-C. Lin | |
| Model averaging prediction for possibly nonstationary autoregressions | |
| A0308: J. Han, A. Alexander John McNeil, A. Dias, M. Bladt | |
| Semiparametric forecasting using non-Gaussian ARMA models based on s-vines |
| Session CC037 | Room: BCB 310 |
| Bayesian statistics | Thursday 24.8.2023 09:00 - 10:00 |
| Chair: Eva Cantoni | Organizer: COMPSTAT |
| A0366: V. Giagos | |
| Bayesian inference of sampling weights in COVID-19 testing | |
| A0335: H. Hachem, I. Albert | |
| PCBs intake assessment using a general Bayesian network depending on the meat safety monitoring system | |
| A0349: H. Pazira, M. Jonker, T. Coolen | |
| Federated Bayesian inference for time-to-event data |
| Session CC034 | Room: BCB 311 |
| Computational and financial econometrics | Thursday 24.8.2023 09:00 - 10:00 |
| Chair: Massimiliano Caporin | Organizer: COMPSTAT |
| A0151: L.A. Arteaga Molina, J.M. Rodriguez-Poo | |
| Testing beta constancy in capital asset pricing models | |
| A0360: N. Ahlgren, A. Back, T. Terasvirta | |
| Sup-tests against time-varying GARCH models | |
| A0153: E. Iglesias | |
| Asymptotic inference for new double autoregressive models |
| Parallel session J: COMPSTAT2023 | Thursday 24.8.2023 | 10:30 - 12:30 |
| Session CO113 | Room: BCB 307 |
| Tutorial II | Thursday 24.8.2023 10:30 - 12:30 |
| Chair: Francisco Javier Rubio | Organizer: COMPSTAT |
| A0406: F.J. Rubio | |
| Bayesian variable selection for survival data: Theory, methods, software and applications |
| Session CO012 | Room: BCB 308 |
| New trends for statistical computing: Bayesian and symbolic data analysis | Thursday 24.8.2023 10:30 - 12:30 |
| Chair: Yuichi Mori | Organizer: Yuichi Mori |
| A0254: J. Nakano, N. Shimizu, Y. Yamamoto | |
| Chestnut plot to visualize aggregated symbolic data | |
| A0187: L.-C. Lin, M. Guo, S. Lee | |
| Monitoring photochemical pollutants based on symbolic interval-valued data analysis | |
| A0211: S.-H. Wang, H.-H. Huang, R. Bai | |
| Mixed-type multivariate Bayesian sparse variable selection with shrinkage priors | |
| A0212: C. Kim | |
| Bayesian nonparametric methods for causal effects with intermediate variables | |
| A0175: M.H. Ling | |
| On reliability analysis of one-shot device testing data with defects |
| A0183: M. Maciak, S. Vitali | |
| Detection and estimation of changepoints within time-dependent functional profiles | |
| A0233: M. Wendler, L. Wegner | |
| Dependent wild bootstrap for change-point detection in functional time series and random fields | |
| A0170: J. Kalina | |
| The minimum weighted covariance determinant estimator for high-dimensional data | |
| A0221: O. Okhrin, M. Fengler | |
| Adaptive factor modeling | |
| A0182: M. Pesta, S. Hudecova | |
| Semi-continuous time series for sparse data with volatility clustering |
| Session CC065 | Room: BCB 309 |
| Robust methods | Thursday 24.8.2023 10:30 - 12:30 |
| Chair: Sara Taskinen | Organizer: COMPSTAT |
| A0273: S.A. Abbasi, M. Amouna | |
| Robust monitoring of process dispersion | |
| A0309: P. Mozharovskyi, J. Ivanovs | |
| Distributionally robust halfspace depth | |
| A0289: M. Marozzi | |
| A robust combined nonparametric method for comparing two locations | |
| A0184: C. Baum, A. Van Messem, H. Cevallos-Valdiviezo | |
| Robustness under missing data: A comparison with special attention to inference | |
| A0339: R. Hieda, S. Yuki, K. Tanioka, H. Yadohisa | |
| Estimation of treatment effects based on robust sparse reduced-rank regression |
| Session CC111 | Room: BCB 311 |
| Applied econometrics | Thursday 24.8.2023 10:30 - 12:30 |
| Chair: Massimiliano Caporin | Organizer: COMPSTAT |
| A0367: E. Gosinska, K. Leszkiewicz-Kedzior, A. Welfe | |
| The asymmetry in the process of price formation: Threshold cointegration analysis | |
| A0402: J.-H. KO | |
| Revisiting the sources of U.S. imbalances: Wavelet approach | |
| A0387: L. Petrasek | |
| US equity announcement risk premia | |
| A0318: J. Kukacka | |
| Good and bad volatility in cryptocurrencies: Connectedness, asymmetry, and their drivers | |
| A0356: P. Caraiani | |
| High frequency financial network connectedness and monetary policy shocks |
| Session CP001 | Room: Poster session |
| Poster Session | Thursday 24.8.2023 10:30 - 12:30 |
| Chair: Cristian Gatu | Organizer: COMPSTAT |
| A0214: M.-S. Oh | |
| BayMDS: An R package for Bayesian multidimensional scaling and choice of dimension | |
| A0267: S. Shvydka, V. Sarabeev, M. Ovcharenko, M. Zdimalova | |
| Modelling symbiotic species richness from invertebrate aquatic hosts using generalized linear and additive models | |
| A0332: L. Sablica, K. Hornik, J. Leydold | |
| watson: An R package for fitting mixtures of Watson distributions | |
| A0385: K. Takahashi | |
| Comparing F1-scores of more than two binary medical tests | |
| A0323: S. Park, A. Bensoussan | |
| Optimal consumption and investment with independent stochastic labor income |
| Parallel session K: COMPSTAT2023 | Thursday 24.8.2023 | 14:15 - 15:45 |
| Session CV032 | Room: BCB 311 |
| Machine learning and computational methods | Thursday 24.8.2023 14:15 - 15:45 |
| Chair: Rosaria Lombardo | Organizer: COMPSTAT |
| A0373: G. Saraceno, M. Markatou | |
| Goodness-of-fit and clustering of spherical and directional data: A comprehensive R package | |
| A0285: A. Bhatti | |
| Fairness in machine learning in the presence of missing values | |
| A0306: M. Savino, C. Levy-Leduc | |
| A novel approach for estimating functions in the multivariate setting based on an adaptive knot selection for B-splines | |
| A0363: S. Hoejsgaard, M.M. Andersen | |
| Computer algebra systems in R |
| Session CI002 (Special Invited Session) | Room: BCB 307 |
| Robust statistics for modern inference problems | Thursday 24.8.2023 14:15 - 15:45 |
| Chair: Eva Cantoni | Organizer: Eva Cantoni |
| A0168: I. Wilms, G. Louvet, J. Raymaekers, G. Van Bever | |
| The influence function of graphical lasso estimators | |
| A0190: D. La Vecchia | |
| Some aspects of robust optimal transportation, with applications to statistics and machine learning | |
| A0280: S. Muller, P. Su, T. Garth, S. Wang | |
| Robust cellwise regularized sparse regression |
| Session CO013 | Room: BCB 308 |
| New developments in Bayesian analysis | Thursday 24.8.2023 14:15 - 15:45 |
| Chair: Ray-Bing Chen | Organizer: Ray-Bing Chen |
| Session CO106 | Room: BCB 310 |
| HiTEc session: Advances in functional data | Thursday 24.8.2023 14:15 - 15:45 |
| Chair: Enea Bongiorno | Organizer: Enea Bongiorno, Kwo Lik Lax Chan |
| A0312: K.L.L. Chan | |
| On specifying a link function of a single functional index model | |
| A0209: K. Hron, C. Genest, J. Neslehova | |
| Orthogonal decomposition of multivariate densities in Bayes spaces in context of functional data analysis | |
| A0222: S. Otto, A. Kneip, D. Liebl | |
| Combining concurrent and functional linear regression | |
| A0245: G. Van Bever, J.M. Jeon | |
| Additive regression with general imperfect variables |
| Session CC050 | Room: BCB 309 |
| Forecasting | Thursday 24.8.2023 14:15 - 15:45 |
| Chair: Nicolas Hernandez | Organizer: COMPSTAT |
| A0374: T. Zahn, M.-O. Pohle | |
| Skill scores, predictive power and limits of predictability | |
| A0392: M. Carannante, V. D Amato, S. Haberman, M. Menzietti | |
| A strong link between mortality projections and frailty in Lee Carter model | |
| A0396: M. Arro-Cannarsa, R. Scheufele | |
| Nowcasting GDP in Switzerland: What are the gains from machine learning algorithms? | |
| A0394: D. Mendes, V. Mendes, N. Ferreira | |
| Multivariate forecast for financial stock prices: A hybrid VAR-LSTM deep learning model |
| Parallel session L: COMPSTAT2023 | Thursday 24.8.2023 | 16:15 - 17:45 |
| Session CV044 | Room: Virtual room R01 |
| Applied statistics and econometrics | Thursday 24.8.2023 16:15 - 17:45 |
| Chair: Rosaria Lombardo | Organizer: COMPSTAT |
| A0357: L. Donayre, L. Loomer | |
| The transitory component of health care employment | |
| A0381: M. Nguyen | |
| Financial distress prediction using machine learning: When Altman meets Merton in a transition economy | |
| A0283: J. Zou, O. Okhrin, M. Odening | |
| Data-driven optimal phase division for improved weather index insurance design | |
| A0313: M. Fayaz | |
| Studying the COVID-19 lockdown effects on Iranian traffic behavior in three calendars with functional data analysis |
| Session CI005 (Special Invited Session) | Room: BCB 307 |
| Bayesian models: Inference and applications | Thursday 24.8.2023 16:15 - 17:45 |
| Chair: Ioanna Manolopoulou | Organizer: Ioanna Manolopoulou |
| A0348: M. Daniels, W. Bae | |
| A Bayesian non-parametric approach for causal mediation with a post-treatment confounder | |
| A0397: R. Hahn | |
| Bayesian regression tree ensembles for survival analysis | |
| A0404: A. Franks, A. Alex | |
| Sensitivity to unobserved confounding in studies with factor-structured outcomes |
| Session CO021 | Room: BCB 309 |
| Statistics and data analytics | Thursday 24.8.2023 16:15 - 17:45 |
| Chair: Stefan Van Aelst | Organizer: Stefan Van Aelst |
| A0317: E. Bongiorno, K.L.L. Chan, A. Goia | |
| Non-parametric dimensionality detection for functional data | |
| A0354: L. Insolia, S. Guerrier, M.-P. Victoria-Feser, Y. Ma, Y. Boulaguiem, D.-L. Couturier | |
| Multivariate finite-sample adjustments for equivalence testing | |
| A0294: R. Yao, J. Raymaekers, P. Rousseeuw, T. Verdonck | |
| Fast linear model trees by PILOT | |
| A0376: S. Van Aelst, A.-A. Christidis, R. Zamar | |
| Subset selection ensembles |
| Session CO023 | Room: BCB 310 |
| HiTEc session: Compositional, distributional and relative abundance data | Thursday 24.8.2023 16:15 - 17:45 |
| Chair: Karel Hron | Organizer: Karel Hron |
| A0210: P. Jaskova, K. Hron, J. Palarea-Albaladejo, M. Templ | |
| Selection of relevant pairwise logratios for high-dimensional compositional data | |
| A0213: V. Nesrstova, I. Wilms, K. Hron, P. Filzmoser | |
| Identification of important pairwise logratios in compositional data employing sparse principal component analysis | |
| A0180: B. Pestova, M. Pesta, M. Maciak | |
| Unsupervised changepoint detection for panel data | |
| A0293: S. Skorna, K. Hron, J. Machalova, J. Burkotova | |
| Approximation of bivariate densities with compositional splines |
| Session CC085 | Room: BCB 308 |
| Computational statistics | Thursday 24.8.2023 16:15 - 17:45 |
| Chair: Mark De Rooij | Organizer: COMPSTAT |
| A0307: J. Guerin, P. Mozharovskyi | |
| A polynomial-time algorithm for optimization-based depths | |
| A0322: T. Ota, K. Okusa | |
| Statistical estimation of heart movements by microwave Doppler radar sensor | |
| A0324: D. Bodenham, Y. Kawahara | |
| Efficient nonparametric two-sample testing with the maximum mean discrepancy | |
| A0303: Q. Clairon, A. Leclercq-Samson | |
| Optimal control for parameter estimation in partially observed hypoelliptic stochastic differential equations |
| Parallel session M: COMPSTAT2023 | Friday 25.8.2023 | 09:00 - 10:00 |
| Session CV035 | Room: Virtual room R01 |
| Computational and financial econometrics | Friday 25.8.2023 09:00 - 10:00 |
| Chair: Alessandra Luati | Organizer: COMPSTAT |
| A0337: B. Kozyrev, O. Holtemoeller | |
| Forecasting economic activity with a neural network in uncertain times: Application to German GDP | |
| A0160: M.M. Pizarro, E.L. Sanjuan, M.I. Parra Arevalo | |
| Informative priors to estimate the value-at-risk |
| Session CO014 | Room: BCB 309 |
| Recent clustering methods for complex data I | Friday 25.8.2023 09:00 - 10:00 |
| Chair: Mika Sato-Ilic | Organizer: Mika Sato-Ilic |
| A0219: S.-K.A. Ng, R. Tawiah, H. Nguyen, F. Forbes | |
| Mixture of linear mixed models for clustering weighted random graphs | |
| A0235: K. Tanioka, H. Yadohisa | |
| Asymmetric cluster difference scaling based on hill-climbing model | |
| A0236: M. Sato-Ilic | |
| A fuzzy cluster-scaled principal component analysis for mixed high-dimension and low-sample size data |
| Session CC045 | Room: BCB 307 |
| Applied statistics and data analysis | Friday 25.8.2023 09:00 - 10:00 |
| Chair: Luca Insolia | Organizer: COMPSTAT |
| A0150: Y.-C.I. Chang | |
| Federated learning via distributed sequential method | |
| A0299: N. Hamed, S. Chan | |
| Composite lognormal distributions of cosmic voids in simulation and mock data | |
| A0411: J. Striaukas | |
| Factor-augmented sparse MIDAS regression for nowcasting |
| Session CC118 | Room: BCB 308 |
| Quality control | Friday 25.8.2023 09:00 - 10:00 |
| Chair: Steven Gilmour | Organizer: COMPSTAT |
| Session CC070 | Room: BCB 310 |
| HiTEc session: Text mining | Friday 25.8.2023 09:00 - 10:00 |
| Chair: Maria Brigida Ferraro | Organizer: COMPSTAT |
| A0281: P. Winker | |
| Visualizing topic uncertainty in topic modelling | |
| A0297: A. Staszewska-Bystrova, V. Bystrov, V. Naboka-Krell, P. Winker | |
| Choosing the number of topics in LDA models: A Monte Carlo comparison of selection criteria | |
| A0331: P. Baranowski, S. Wojcik | |
| Textual content and academic journals selectiveness: A case of economic journals |
| Session CC094 | Room: BCB 311 |
| Longitudinal and functional data analysis | Friday 25.8.2023 09:00 - 10:00 |
| Chair: Sonja Greven | Organizer: COMPSTAT |
| A0220: K. Hayakawa, B. Yin | |
| The mean group estimators for multi-level autoregressive models with intensive longitudinal data | |
| A0355: A. Eletti, G. Marra, R. Radice | |
| General estimation framework for multi-state Markov processes with flexible specification of the transition intensities | |
| A0405: M. Ofner, S. Hoermann | |
| Reconstructing partially observed functional data via factor models of increasing rank |
| Parallel session N: COMPSTAT2023 | Friday 25.8.2023 | 10:30 - 12:00 |
| Session CV031 | Room: BCB 308 |
| Time series and dependence models | Friday 25.8.2023 10:30 - 12:00 |
| Chair: Alessandra Luati | Organizer: COMPSTAT |
| A0344: K.W. Chan, H.K. To | |
| Mean stationarity test in time series: A signal variance-based approach | |
| A0342: M.D.C. Robustillo Carmona, L. Naranjo Albarran, M.I. Parra Arevalo, C.J. Perez Sanchez | |
| A vector error correction model to address sensor-based time series | |
| A0371: M. Dolfin, J. De Leon Miranda | |
| Exploring the impact of non-linear dependencies in stock market returns regime transitions | |
| A0375: M. Borsch, A. Mayer, D. Wied | |
| Consistent estimation of multiple breakpoints in dependence measures |
| Session CI007 (Special Invited Session) | Room: BCB 310 |
| HiTEc session: Recent advances in dimension reduction methods | Friday 25.8.2023 10:30 - 12:00 |
| Chair: Sara Taskinen | Organizer: Sara Taskinen |
| A0192: A. Artemiou, C. Antonis | |
| An adaptive approach for sparse quantile regression | |
| A0196: K. Nordhausen, A. Alfons, A. Archimbaud, A. Ruiz-Gazen | |
| Tandem clustering with ICS | |
| A0229: S. De Iaco | |
| Spatio-temporal coregionalization modeling by using simultaneous diagonalization |
| Session CO022 | Room: BCB 307 |
| Statistics applied to industry | Friday 25.8.2023 10:30 - 12:00 |
| Chair: Francisco Louzada | Organizer: Francisco Louzada |
| A0338: F. Louzada | |
| Reliability in Brazil: Roads for approaching industry | |
| A0343: P. Ferreira, E. Brito, V. Tomazella, F. Louzada, O. Gonzatto-Junior | |
| Statistical modeling and reliability analysis of repairable systems with dependent failure times under imperfect repair | |
| A0276: D. Nascimento | |
| Stats in Industry 5.0: Some cases of contemporaneous experimental designs adopting dynamic and hierarchical structures | |
| A0407: P. Ramos | |
| Statistical inference for generalized power-law process in repairable systems |
| Session CO020 | Room: BCB 309 |
| Recent clustering methods for complex data II | Friday 25.8.2023 10:30 - 12:00 |
| Chair: Mika Sato-Ilic | Organizer: Mika Sato-Ilic |
| A0249: M. Ohishi, H. Yanagihara | |
| Clustering for category variables in linear regression via generalized fused Lasso | |
| A0250: K. Kirishima, M. Ohishi, H. Yanagihara | |
| Comparison of prediction methods for spatial data using real estate data | |
| A0268: C. Marsala | |
| Subclass discovery from fuzzy decision trees |
| Session CO018 | Room: BCB 311 |
| ML and FinTech | Friday 25.8.2023 10:30 - 12:00 |
| Chair: Maria Grith | Organizer: Ying Chen, Maria Grith |
| A0205: G. Finocchio, J. Schmidt-Hieber | |
| Posterior contraction for deep Gaussian process priors | |
| A0238: R. Miftachov, M. Grith, Z. Wang | |
| On pricing kernels for digital assets | |
| A0260: H.L.H. Lai, M. Grith, Y. Chen | |
| Modeling nonlinear dynamics of functional time series for large-scale data | |
| A0264: M. Grith | |
| Spectral factors for functional data |
| Session CO010 | Room: Virtual room R01 |
| Recent advances in Bayesian econometrics | Friday 25.8.2023 10:30 - 12:00 |
| Chair: TBA | Organizer: |
| A0274: M. Takahashi | |
| Analyzing intraday variation in price impact: A Bayesian SVAR approach with stochastic volatility estimation | |
| A0277: J. Nakajima | |
| Time-varying parameter local projections with stochastic volatility | |
| A0279: T. Kano | |
| Posterior inferences on incomplete structural models: The minimal econometric interpretation | |
| A0278: J. Stroud, M. Johannes, N.J. Seeger | |
| Time-varying macroeconomic announcement risk |