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August 2010 Optimal rates of convergence for covariance matrix estimation
T. Tony Cai, Cun-Hui Zhang, Harrison H. Zhou
Ann. Statist. 38(4): 2118-2144 (August 2010). DOI: 10.1214/09-AOS752

Abstract

Covariance matrix plays a central role in multivariate statistical analysis. Significant advances have been made recently on developing both theory and methodology for estimating large covariance matrices. However, a minimax theory has yet been developed. In this paper we establish the optimal rates of convergence for estimating the covariance matrix under both the operator norm and Frobenius norm. It is shown that optimal procedures under the two norms are different and consequently matrix estimation under the operator norm is fundamentally different from vector estimation. The minimax upper bound is obtained by constructing a special class of tapering estimators and by studying their risk properties. A key step in obtaining the optimal rate of convergence is the derivation of the minimax lower bound. The technical analysis requires new ideas that are quite different from those used in the more conventional function/sequence estimation problems.

Citation

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T. Tony Cai. Cun-Hui Zhang. Harrison H. Zhou. "Optimal rates of convergence for covariance matrix estimation." Ann. Statist. 38 (4) 2118 - 2144, August 2010. https://doi.org/10.1214/09-AOS752

Information

Published: August 2010
First available in Project Euclid: 11 July 2010

zbMATH: 1202.62073
MathSciNet: MR2676885
Digital Object Identifier: 10.1214/09-AOS752

Subjects:
Primary: 62H12
Secondary: 62F12, 62G09

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.38 • No. 4 • August 2010
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