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

Mengxi Yi (u. a.)

ISBN 10: 3031226860 / ISBN 13: 9783031226861

Published by Springer, 2023

About this Item

Robust and Multivariate Statistical Methods | Festschrift in Honor of David E. Tyler | Mengxi Yi (u. a.) | Buch | xviii | Englisch | 2023 | Springer | EAN 9783031226861 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Seller Inventory # 125797171

Bibliographic Details

• Title: Robust and Multivariate Statistical Methods | Festschrift in Honor of David E. Tyler
• Publisher: Springer
• Publication Date: 2023
• Language: English
• ISBN 10: 3031226860
• ISBN 13: 9783031226861
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 34D millimeters 241 millimeters width by 160 millimeters height by 34 millimeters depth
• Weight: 934 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

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.