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Optimal rates of convergence for covariance matrix estimation (2010)
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| Venue: | Ann. Statist |
| Citations: | 88 - 19 self |
BibTeX
@ARTICLE{Cai10optimalrates,
author = {T. Tony Cai and Cun-hui Zhang and Harrison and H. Zhou},
title = {Optimal rates of convergence for covariance matrix estimation},
journal = {Ann. Statist},
year = {2010}
}
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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. 1. Introduction. Suppose
Keyphrases
optimal rate operator norm covariance matrix vector estimation optimal procedure key step minimax theory frobenius norm minimax upper bound new idea risk property conventional function sequence estimation problem special class central role technical analysis multivariate statistical analysis significant advance matrix estimation large covariance matrix
