Statistics Papers

Document Type

Journal Article

Date of this Version

2012

Publication Source

Statistical Science

Volume

27

Issue

1

Start Page

82

Last Page

94

DOI

10.1214/11-STS383

Abstract

In a remarkable series of papers beginning in 1956, Charles Stein set the stage for the future development of minimax shrinkage estimators of a multivariate normal mean under quadratic loss. More recently, parallel developments have seen the emergence of minimax shrinkage estimators of multivariate normal predictive densities under Kullback–Leibler risk. We here describe these parallels emphasizing the focus on Bayes procedures and the derivation of the superharmonic conditions for minimaxity as well as further developments of new minimax shrinkage predictive density estimators including multiple shrinkage estimators, empirical Bayes estimators, normal linear model regression estimators and nonparametric regression estimators.

Keywords

asymptotic minimaxity, Bayesian prediction, empirical Bayes, inadmissibility, multiple shrinkage, prior distributions, superharmonic marginals, unbiased estimates of risk

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Date Posted: 27 November 2017

This document has been peer reviewed.