Annals of Statistics

Estimation of functionals of sparse covariance matrices

Abstract

High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other $\ell_{r}$ norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics.

Article information

Source
Ann. Statist., Volume 43, Number 6 (2015), 2706-2737.

Dates
Revised: June 2015
First available in Project Euclid: 7 October 2015

https://projecteuclid.org/euclid.aos/1444222090

Digital Object Identifier
doi:10.1214/15-AOS1357

Mathematical Reviews number (MathSciNet)
MR3405609

Zentralblatt MATH identifier
1327.62338

Citation

Fan, Jianqing; Rigollet, Philippe; Wang, Weichen. Estimation of functionals of sparse covariance matrices. Ann. Statist. 43 (2015), no. 6, 2706--2737. doi:10.1214/15-AOS1357. https://projecteuclid.org/euclid.aos/1444222090

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Supplemental materials

• Technical proofs Fan, Rigollet and Wang (2015) . This supplementary material contains the introduction to two-sample high-dimensional testing methods and the proofs of upper bounds that were omitted from the paper.