Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1146367 | Journal of Multivariate Analysis | 2011 | 12 Pages |
Abstract
This paper addresses the problem of estimating the density of a future outcome from a multivariate normal model. We propose a class of empirical Bayes predictive densities and evaluate their performances under the Kullback–Leibler (KL) divergence. We show that these empirical Bayes predictive densities dominate the Bayesian predictive density under the uniform prior and thus are minimax under some general conditions. We also establish the asymptotic optimality of these empirical Bayes predictive densities in infinite-dimensional parameter spaces through an oracle inequality.
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Xinyi Xu, Dunke Zhou,