.png){kind=icon}
返回
Stein Estimation for Spherically Symmetric Distributions: Recent Developments
.png){kind=icon}
OA
AI
DOI：10.1214/10-STS323.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper reviews advances in Stein-type shrinkage estimation for spherically symmetric distributions. Some emphasis is placed on developing intuition as to why shrinkage should work in location problems whether the underlying population is normal or not. Considerable attention is devoted to generalizing the Stein lemma which underlies much of the theoretical development of improved minimax estimation for spherically symmetric distributions. A main focus is on distributional robustness results in cases where a residual vector is available to estimate an unknown scale parameter, and, in particular, in finding estimators which are simultaneously generalized Bayes and minimax over large classes of spherically symmetric distributions. Some attention is also given to the problem of estimating a location vector restricted to lie in a polyhedral cone.
Keyword:
Stein estimation
spherical symmetry
minimaxity
admissibility
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.4
论文数:
1.0K
被引数:
8.7K
机构
引用论文
分享收藏Posterior propriety and admissibility of hyperpriors in normal hierarchical models正常分层模型中先验的后验性和可容许性.png)
ANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏
查看更多内容