Regularized LRT for Large Scale Covariance Matrices: One Sample Problem

TL;DR

This paper introduces a regularized likelihood ratio test (LRT) for high-dimensional covariance matrices that improves power over existing corrected LRT by incorporating shrinkage estimators, with proven asymptotic distributions.

Contribution

It proposes a novel regularized LRT using shrinkage estimators, providing better differentiation of covariance hypotheses in high-dimensional settings.

Findings

Regularized LRT outperforms corrected LRT in simulations.

Asymptotic distribution derived for identity and spiked covariance matrices.

Enhanced power compared to recent non-likelihood procedures.

Abstract

The main theme of this paper is a modification of the likelihood ratio test (LRT) for testing high dimensional covariance matrix. Recently, the correct asymptotic distribution of the LRT for a large-dimensional case (the case $p/n$ approaches to a constant $\gamma \in (0,1]$) is specified by researchers. The correct procedure is named as corrected LRT. Despite of its correction, the corrected LRT is a function of sample eigenvalues that are suffered from redundant variability from high dimensionality and, subsequently, still does not have full power in differentiating hypotheses on the covariance matrix. In this paper, motivated by the successes of a linearly shrunken covariance matrix estimator (simply shrinkage estimator) in various applications, we propose a regularized LRT that uses, in defining the LRT, the shrinkage estimator instead of the sample covariance matrix. We compute the asymptotic distribution of the regularized LRT, when the true covariance matrix is the identity matrix and a spiked covariance matrix. The obtained asymptotic results have applications in testing various hypotheses on the covariance matrix. Here, we apply them to testing the identity of the true covariance matrix, which is a long standing problem in the literature, and show that the regularized LRT outperforms the corrected LRT, which is its non-regularized counterpart. In addition, we compare the power of the regularized LRT to those of recent non-likelihood based procedures.