Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5129387 | Journal of Multivariate Analysis | 2017 | 13 Pages |
Abstract
This paper deals with the problem of estimating predictive densities of a matrix-variate normal distribution with known covariance matrix. Our main aim is to establish some Bayesian predictive densities related to matricial shrinkage estimators of the normal mean matrix. The Kullback-Leibler loss is used for evaluating decision-theoretic optimality of predictive densities. It is shown that a proper hierarchical prior yields an admissible and minimax predictive density. Also, some minimax predictive densities are derived from superharmonicity of prior densities.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Hisayuki Tsukuma, Tatsuya Kubokawa,