The Annals of Statistics

A Distribution-Free $M$-Estimator of Multivariate Scatter

David E. Tyler

Full-text: Open access

Abstract

The existence and uniqueness of a limiting form of a Huber-type $M$-estimator of multivariate scatter is established under certain conditions on the observed sample. These conditions hold with probability one when sampling randomly from a continuous multivariate distribution. The existence of the estimator is proven by showing that it is the limiting point of a specific algorithm. Hence, the proof is constructive. For continuous populations, the estimator of multivariate scatter is shown to be strongly consistent and asymptotically normal. An important property of the estimator is that its asymptotic distribution is distribution-free with respect to the class of continuous elliptically distributed populations. This distribution-free property also holds for the finite sample size distribution when the location parameter is known. In addition, the estimator is the "most robust" estimator of the scatter matrix of an elliptical distribution in the sense of minimizing the maximum asymptotic variance.

Article information

Source
Ann. Statist., Volume 15, Number 1 (1987), 234-251.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176350263

Digital Object Identifier
doi:10.1214/aos/1176350263

Mathematical Reviews number (MathSciNet)
MR885734

Zentralblatt MATH identifier
0628.62053

JSTOR
links.jstor.org

Subjects
Primary: 62H12: Estimation
Secondary: 62H10: Distribution of statistics 62G05: Estimation

Keywords
Covariance matrix distribution-free elliptical distribution $M$-estimation multivariate scatter pseudocovariance matrix robustness

Citation

Tyler, David E. A Distribution-Free $M$-Estimator of Multivariate Scatter. Ann. Statist. 15 (1987), no. 1, 234--251. doi:10.1214/aos/1176350263. https://projecteuclid.org/euclid.aos/1176350263


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