Robust and Multivariate Statistical Methods: Festschrift in Honor of David E. Tyler

The book covers recent developments in multivariate and robust statistical methods, featuring contributions by leading experts, and is dedicated to David E. Tyler on the occasion of his pending retirement.

Format: Hardback
Length: 495 pages
Publication date: 20 April 2023
Publisher: Springer International Publishing AG

This comprehensive book delves into the latest advancements in multivariate and robust statistical methods, presenting insightful contributions from esteemed experts in the field. Spanning a wide range of topics, including multivariate and high-dimensional techniques, time series analysis, graphical models, robust estimation, supervised learning, and normal extremes, it caters to the interests of statistics and data science researchers, Ph.D. students, and practitioners seeking to stay abreast of modern multivariate and robust statistics. In honor of David E. Tyler's impending retirement, the book is dedicated to him, and it also features a review contribution on the widely recognized Tylers shape matrix.

The book begins with an introductory chapter that provides a foundational overview of multivariate and robust statistics, highlighting their importance in modern data analysis. It then proceeds to explore various topics in depth, offering detailed explanations and practical examples to facilitate a better understanding of the subject matter.

One of the key strengths of the book is its extensive coverage of multivariate and high-dimensional methods. It discusses various techniques such as principal component analysis, factor analysis, latent variable models, and non-linear dimensionality reduction, providing practical insights into their applications and limitations. The authors also discuss the use of these methods in various fields, including finance, biology, and social sciences.

Time series analysis is another area of focus, with chapters dedicated to the analysis of stationary and non-stationary time series data. The book covers topics such as autoregressive models, moving average models, and ARMA models, as well as the application of these models in forecasting and anomaly detection. Graphical models, which are a powerful tool for representing and analyzing complex relationships between variables, are also explored in detail. The authors discuss various graphical models, including Bayesian networks, decision trees, and Markov chains, and demonstrate their usefulness in various domains such as medicine, social networks, and marketing.

Robust estimation is a crucial aspect of statistical analysis, particularly in the presence of noise and outliers. The book provides comprehensive coverage of robust methods, including the use of robust statistics, bootstrapping, and simulation-based methods. It discusses the advantages and limitations of each method and demonstrates their application in real-world datasets. Supervised learning, a fundamental technique in machine learning, is also discussed in the book. It covers topics such as linear regression, logistic regression, neural networks, and support vector machines, providing practical insights into their implementation and evaluation.

Normal extremes, a topic of growing interest in statistics and data science, are also explored in the book. It discusses the theory and applications of extreme value theory, including the estimation of extreme values, the analysis of extreme events, and the development of risk models for extreme events. The authors also discuss the use of Bayesian methods in normal extremes analysis, providing a comprehensive overview of the subject matter.

In addition to the technical discussions, the book includes a review contribution on the popular Tylers shape matrix. The Tylers shape matrix is a widely used tool in multivariate analysis, particularly in the analysis of contingency tables. The review provides an overview of the matrix's history, its properties, and its applications in various fields.

Overall, this book is a valuable resource for statistics and data science researchers, Ph.D. students, and practitioners who are interested in modern multivariate and robust statistics. It provides a comprehensive and up-to-date coverage of the field, covering a wide range of topics and presenting insightful contributions from leading experts in the field. The book is well-organized, accessible, and written in a clear and concise manner, making it an ideal tool for both beginners and experienced practitioners.

Weight: 934g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031226861
Edition number: 1st ed. 2023