Overview
- Presents the latest findings in multivariate and robust statistics
- Features contributions by leading experts in the field, including a review of Tyler’s shape matrix
- Fosters new directions of research
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About this book
This book presents recent developments in multivariate and robust statistical methods. Featuring contributions by leading experts in the field it covers various topics, including multivariate and high-dimensional methods, time series, graphical models, robust estimation, supervised learning and normal extremes. It will appeal to statistics and data science researchers, PhD students and practitioners who are interested in modern multivariate and robust statistics. The book is dedicated to David E. Tyler on the occasion of his pending retirement and also includes a review contribution on the popular Tyler’s shape matrix.
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Table of contents (22 chapters)
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Front Matter
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About David E. Tyler’s Publications
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Front Matter
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Multivariate Theory and Methods
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Front Matter
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Robust Theory and Methods
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Front Matter
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Editors and Affiliations
About the editors
Mengxi Yi is an Assistant Professor at the School of Statistics at the Beijing Normal University, Beijing, China. Her primary research interests include multivariate and robust statistics and time series analysis.
Klaus Nordhausen is a University Lecturer in Statistics at the Department of Mathematics and Statistics at the University of Jyväskylä, Finland. His main research interests include supervised and unsupervised dimension reduction, blind source separation, independent components analysis, robust and nonparametric methods and computational statistics.
Bibliographic Information
Book Title: Robust and Multivariate Statistical Methods
Book Subtitle: Festschrift in Honor of David E. Tyler
Editors: Mengxi Yi, Klaus Nordhausen
DOI: https://doi.org/10.1007/978-3-031-22687-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-22686-1Published: 20 April 2023
Softcover ISBN: 978-3-031-22689-2Published: 21 April 2024
eBook ISBN: 978-3-031-22687-8Published: 19 April 2023
Edition Number: 1
Number of Pages: XVIII, 495
Number of Illustrations: 19 b/w illustrations, 95 illustrations in colour
Topics: Statistical Theory and Methods, Applied Statistics, Machine Learning, Machine Learning