Stochastic Models, Statistics and Their Applications by Steland, Ansgar; Rafajłowicz, Ewaryst; Parolya, Nestor;

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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.

Tartalomjegyzék:

- 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.