Improvement of some multidimensional estimates by reduction of dimensionality
type: Item
instance of: scholarly article
title: Improvement of some multidimensional estimates by reduction of dimensionality
zbMATH Open document ID: 0845.62042
author: Louis Ferré
published in: Journal of Multivariate Analysis
publication date: 1996-09-18T00:00:00Z
review text: Consider \(k\) independent \(p\)-dimensional random vectors \(Y_i\) for \(i= 1,\dots, k\), with mean \(\mu_i\) and covariance matrix \(\Sigma\) (or equivalently a \((k\times p)\) random matrix \(Y\) with mean \(\mu\) such that \(\mu^T= [\mu_1, \dots, \mu_p]\) and covariance matrix \(I_k \otimes \Sigma\)). The problem we are dealing with is the estimation of the mean matrix \(\mu\). When \(Y\) is normally distributed and \(\Sigma\) is known, it has been proved [see, e.g., \textit{C. Stein}, Proc. 3rd Berkeley Symp. Math. Stat. Probab. 1, 197-206 (1956; Zbl 0073.35602)] that the sample mean is not admissible in the sense of larger mean square error when \(p\) is strictly greater than two. Stein also proposed in this paper an estimate which might dominate the least squares estimate. Since that time, considerable effort has gone into improving the James-Stein estimate, particularly by considering unknown \(\Sigma\). These works actually aim at proposing classes of estimates (essentially refinements of the basic James-Stein estimate) depending on a real value function. We focus on a different class of estimates. Indeed, if we denote by \(\widehat {\mu}_i\), the \(i\)th row of the sample mean, we investigate estimates of the form \(\widehat {\pi}_m (X_i)+ \overline {\mu}\), where \(\widehat {\pi}_m\) is an orthogonal projection onto an \(m\)-dimensional subspace of \({\mathfrak R}^p\), \(\overline {\mu}\) is a weighted average of the \(\widehat {\mu}_i\)'s and \(X_i= \widehat {\mu}_i- \overline {\mu}\). Actually we will deal with a more general framework. Moreover, we do not assume that the data are necessarily normal, nor do we restrict ourselves to estimating the mean matrix. It is sufficient here to proceed with an unbiased estimate of \(\mu\) and a consistent and unbiased estimate of \(\Sigma\). This more general model will be presented in Section 2, where a way of reaching optimal projections is proposed. Since we aim at deriving an optimal estimate (in the sense of …
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 62F12
Mathematics Subject Classification ID: 62H25
zbMATH DE Number: 797146
zbMATH Keywords: unbiased estimates
zbMATH Keywords: projections
zbMATH Keywords: mean matrix
zbMATH Keywords: optimal estimate
zbMATH Keywords: minimum mean square error
zbMATH Keywords: asymptotic expansions
zbMATH Keywords: risk functions
MaRDI profile type: Publication
full work available at URL: jmva.1995.1049
OpenAlex ID: W2093155715
DOI: 10.1006/JMVA.1995.1049
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description: scientific article; zbMATH DE number 797146