Overview
- Integrates modern and classical shrinkage estimation and contributes to further developments in the field
- Presents direct proof of Brown’s 1971 seminal work on determination of admissibility of generalized Bayes estimators
- Presents recent results of admissibility of generalized Bayes estimators in the presence of a nuisance scale parameter
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
Part of the book sub series: JSS Research Series in Statistics (JSSRES)
Buy print copy
About this book
Keywords
Table of contents (3 chapters)
Authors and Affiliations
About the authors
Yuzo Maruyama is Professor of Statistics at Kobe University. He earned his M.S. and Ph.D. degrees, both in Economics, at the University of Tokyo. His research interests include statistical decision theory, shrinkage estimation, and Bayesian model selection.
Tatsuya Kubokawa is Professor in the Faculty of Economics at the University of Tokyo. He earned his M.S. and Ph.D. degrees, both in Mathematics, at University of Tsukuba. His research interests include statistical decision theory, multivariate analysis, and mixed-effects modeling.
William E. Strawderman is Professor of Statistics at Rutgers University. He earned an M.S. in Mathematics from Cornell University and a second M.S. in Statistics from Rutgers and then completed his Ph.D. in Statistics, also at Rutgers. He is Fellow of both the Institute of Mathematical Statistics and American Statistical Society and Elected Member at International Statistical Institute. His research interests include statistical decision theory, shrinkage estimation, and Bayesian statistics.
Bibliographic Information
Book Title: Stein Estimation
Authors: Yuzo Maruyama, Tatsuya Kubokawa, William E. Strawderman
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-981-99-6077-4
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023
Softcover ISBN: 978-981-99-6076-7Published: 02 October 2023
eBook ISBN: 978-981-99-6077-4Published: 29 September 2023
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: VIII, 130
Number of Illustrations: 3 b/w illustrations
Topics: Applied Statistics, Statistical Theory and Methods, Bayesian Inference, Statistical Theory and Methods