Stochastic Models, Statistics and Their Applications : SMSA 2024, Delft, The Netherlands, March 13-15 / edited by Ansgar Steland, Ewaryst Rafajłowicz, Nestor Parolya.

Format:

Book

Author/Creator:

Steland, Ansgar.

Contributor:

Rafajłowicz, Ewaryst.

Parolya, Nestor.

Series:

Springer Proceedings in Mathematics & Statistics, 2194-1017 ; 499

Language:

English

Subjects (All):

Statistics.

Stochastic models.

Machine learning.

Mathematical statistics.

Statistical Theory and Methods.

Stochastic Modelling in Statistics.

Statistical Learning.

Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Statistics in Business, Management, Economics, Finance, Insurance.

Mathematical Statistics.

Local Subjects:

Statistical Theory and Methods.

Stochastic Modelling in Statistics.

Statistical Learning.

Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Statistics in Business, Management, Economics, Finance, Insurance.

Mathematical Statistics.

Physical Description:

1 online resource (328 pages)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

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.

Contents:

- 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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-031-96015-7

9783031960154

OCLC:

1572193584