Admissibility and minimaxity of Bayes estimators for a normal mean matrix

12Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.

Abstract

In some invariant estimation problems under a group, the Bayes estimator against an invariant prior has equivariance as well. This is useful notably for evaluating the frequentist risk of the Bayes estimator. This paper addresses the problem of estimating a matrix of means in normal distributions relative to quadratic loss. It is shown that a matricial shrinkage Bayes estimator against an orthogonally invariant hierarchical prior is admissible and minimax by means of equivariance. The analytical improvement upon every over-shrinkage equivariant estimator is also considered and this paper justifies the corresponding positive-part estimator preserving the order of the sample singular values. © 2008 Elsevier Inc. All rights reserved.

Cite

CITATION STYLE

APA

Tsukuma, H. (2008). Admissibility and minimaxity of Bayes estimators for a normal mean matrix. Journal of Multivariate Analysis, 99(10), 2251–2264. https://doi.org/10.1016/j.jmva.2008.02.012

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free