Statistics Papers

Document Type

Journal Article

Date of this Version

5-2008

Publication Source

The Annals of Statistics

Volume

36

Issue

3

Start Page

1156

Last Page

1170

DOI

10.1214/07-AOS506

Abstract

Let X|μ∼Np(μ, vxI) and Y|μ∼Np(μ, vyI) be independent p-dimensional multivariate normal vectors with common unknown mean μ. Based on observing X=x, we consider the problem of estimating the true predictive density p(y|μ) of Y under expected Kullback–Leibler loss. Our focus here is the characterization of admissible procedures for this problem. We show that the class of all generalized Bayes rules is a complete class, and that the easily interpretable conditions of Brown and Hwang [Statistical Decision Theory and Related Topics (1982) III 205–230] are sufficient for a formal Bayes rule to be admissible.

Copyright/Permission Statement

The original and published work is available at: https://projecteuclid.org/euclid.aos/1211819560#abstract

Keywords

admissibility, Bayesian predictive distribution, complete class, prior distributions

Share

COinS
 

Date Posted: 27 November 2017

This document has been peer reviewed.