Stochastic Models, Statistics and Their Applications
Description
This volume presents contemporary research in stochastic modeling, statistical inference and their applications, and collects peer-reviewed contributions presented at the 15th Workshop on Stochastic Models, Statistics and Their Applications, SMSA 2024, held in Delft, The Netherlands, March 13-15, 2024. It brings together a unique mix of authors, working on theoretical and applied problems, and addresses a wide variety of topics from the workshop's focus areas, which included Bayesian methods, change point analysis, computational statistics, econometrics, high-dimensional, nonparametric and spatial statistics, statistical process monitoring, statistics for stochastic processes, and sequential and time series analysis. The volume is structured in three parts, covering stochastics and statistical theory, statistical inference and machine learning, and testing for patterns in data. The contributions discuss highly active research topics, such as strong approximation in high dimensions, modeling and testing multivariate distributions, the interplay and fusion of statistical ideas and machine learning, approaches to handling discrete and ordinal data, and detection of hidden patterns in data, with applications to environmental science, business and engineering.
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Persons
Ansgar Steland is a Full Professor at the Institute of Statistics and a member of the AI Center at RWTH Aachen University, Germany. Previously he held positions at the TU Berlin, the European University Viadrina and the Ruhr-University Bochum. He is an Elected Member of the International Statistical Institute (ISI), Chair of the Society for Reliability, Quality and Safety and Vice Chair of the German Statistical Society's Statistics in Natural Sciences and Technology Section. His main research interests include high-dimensional statistics, nonparametrics and empirical processes, time series analysis and spatial statistics, change-point detection and sequential analysis, finance and econometrics, machine learning and artificial intelligence.
Ewaryst Rafajlowicz is a Full Professor at the Department of Control Systems and Mechatronics, Wroclaw University of Science and Technology, Poland. He is a Senior Member of IEEE and the American Mathematical Society, and an elected Corresponding Member of the Polish Academy of Sciences. He has been a Visiting Professor at many universities in the USA, Canada, Germany and the UK, and has served on the program committees of several international conferences and as a reviewer for many journals. His main research interests include statistical quality control, optimal design of experiments, nonparametric regression estimation, estimation of parameters of PDEs, and signal and image processing.
Nestor Parolya is an Assistant Professor in Statistics (tenured) at the Institute of Applied Mathematics, Delft University of Technology, the Netherlands. Previously he was an Assistant Professor at Leibniz University Hannover and a PostDoc at the Ruhr-University Bochum. He has been a Visiting Professor at Heidelberg and Mannheim universities. His research interests include high-dimensional statistics, large dimensional random matrix theory, high-dimensional statistical learning, statistical/mathematical finance, financial engineering and operations research. He received the Wolfgang-Wetzel-Preis from the German Statistical Society for an outstanding contribution to the statistical methodology and its application in 2019.
Content
- Part I: Stochastics and Statistical Theory .- Strong Gaussian Approximations with Random Multipliers.- Selection of Parametric Copula Models in the Approximation of Copulas using Cramér-von Mises Divergence.- Multivariate Dependence Based on Diagonal Sections: Spearman's Footrule and Related Measures.- Proportional Asymptotics of Piecewise Exponential Proportional Hazards Models.- On the Choice of the Two Tuning Parameters for Nonparametric Estimation of an Elliptical Distribution Generator.- Part II: Inference and Machine Learning .- Inference from Longitudinal Data by Clustering and Machine Learning.- The Use of Neural Networks and PCA Dimensionality Reduction in the Imputation of Missing Fragments in High-Dimensional Time Series.- Discrete-Valued Time Series and Recurrent Neural Network Response Functions.- Application of Model-Free Time-Series Segmentation to Study Sleep in Mice.- Part III: Detection of Patterns in Data .- Testing for Dependence by Using Ordinal Patterns: Survey and Perspectives.- On Some Properties and Testing of Benford's Law.