A shrinkage predictive distribution for multivariate Normal observables

被引：60

作者：

Komaki, F ^{[1
]}

机构：

[1] Univ Tokyo, Grad Sch Informat Sci & Technol, Dept Math Informat, Bunkyo Ku, Tokyo 1130033, Japan

来源：

BIOMETRIKA | 2001年 / 88卷 / 03期

关键词：

invariance; James-Stein estimator; Kullback-Leibler divergence; Stein's prior; vague prior;

D O I：

10.1093/biomet/88.3.859

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

We investigate shrinkage methods for constructing predictive distributions. We consider the multivariate Normal model with a known covariance matrix and show that there exists a shrinkage predictive distribution dominating the Bayesian predictive distribution based on the vague prior when the dimension is not less than three. Kullback-Leibler divergence from the true distribution to a predictive distribution is adopted as a loss function.

引用

页码：859 / 864

页数：6