| 书目名称 | Shrinkage Estimation for Mean and Covariance Matrices |
| 编辑 | Hisayuki Tsukuma,Tatsuya Kubokawa |
| 视频video | |
| 概述 | Integrates modern and classical shrinkage estimation and contributes to further developments in the field.Provides a unified approach to low- and high-dimensional models with respect to the size of th |
| 丛书名称 | SpringerBriefs in Statistics |
| 图书封面 |  |
| 描述 | .This book provides a self-contained introduction to shrinkage estimation for matrix-variate normal distribution models. More specifically, it presents recent techniques and results in estimation of mean and covariance matrices with a high-dimensional setting that implies singularity of the sample covariance matrix. Such high-dimensional models can be analyzed by using the same arguments as for low-dimensional models, thus yielding a unified approach to both high- and low-dimensional shrinkage estimations. The unified shrinkage approach not only integrates modern and classical shrinkage estimation, but is also required for further development of the field. Beginning with the notion of decision-theoretic estimation, this book explains matrix theory, group invariance, and other mathematical tools for finding better estimators. It also includes examples of shrinkage estimators for improving standard estimators, such as least squares, maximum likelihood, and minimum risk invariantestimators, and discusses the historical background and related topics in decision-theoretic estimation of parameter matrices. This book is useful for researchers and graduate students in various fields requir |
| 出版日期 | Book 2020 |
| 关键词 | Covariance Matrix; Empirical Bayes; High-dimensional Model; James-Stein Sstimator; Linear Model; Shrinkag |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-981-15-1596-5 |
| isbn_softcover | 978-981-15-1595-8 |
| isbn_ebook | 978-981-15-1596-5Series ISSN 2191-544X Series E-ISSN 2191-5458 |
| issn_series | 2191-544X |
| copyright | The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 |
发表于 2025-3-21 16:32:26
