The Annals of Statistics

Improved multivariate normal mean estimation with unknown covariance when $p$ is greater than $n$

Didier Chételat and Martin T. Wells

Full-text: Open access

Abstract

We consider the problem of estimating the mean vector of a $p$-variate normal $(\theta,\Sigma)$ distribution under invariant quadratic loss, $(\delta-\theta)'\Sigma^{-1}(\delta-\theta)$, when the covariance is unknown. We propose a new class of estimators that dominate the usual estimator $\delta^{0}(X)=X$. The proposed estimators of $\theta$ depend upon $X$ and an independent Wishart matrix $S$ with $n$ degrees of freedom, however, $S$ is singular almost surely when $p>n$. The proof of domination involves the development of some new unbiased estimators of risk for the $p>n$ setting. We also find some relationships between the amount of domination and the magnitudes of $n$ and $p$.

Article information

Source
Ann. Statist., Volume 40, Number 6 (2012), 3137-3160.

Dates
First available in Project Euclid: 22 February 2013

Permanent link to this document
https://projecteuclid.org/euclid.aos/1361542077

Digital Object Identifier
doi:10.1214/12-AOS1067

Mathematical Reviews number (MathSciNet)
MR3097972

Zentralblatt MATH identifier
1296.62048

Subjects
Primary: 62F10: Point estimation
Secondary: 62C20: Minimax procedures 62H12: Estimation

Keywords
Covariance estimation James–Stein estimation invariant quadratic loss large-$p$–small-$n$ problems location parameter minimax estimation Moore–Penrose inverse risk function singular Wishart distribution

Citation

Chételat, Didier; Wells, Martin T. Improved multivariate normal mean estimation with unknown covariance when $p$ is greater than $n$. Ann. Statist. 40 (2012), no. 6, 3137--3160. doi:10.1214/12-AOS1067. https://projecteuclid.org/euclid.aos/1361542077


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