Corrections to LRT on Large Dimensional Covariance Matrix by RMT

TL;DR

This paper identifies why traditional likelihood ratio tests fail in high-dimensional covariance matrix scenarios and proposes corrected tests based on random matrix theory, improving accuracy for large p and non-Gaussian data.

Contribution

The paper introduces novel corrections to likelihood ratio tests for high-dimensional covariance matrices using recent RMT results, applicable to Gaussian and non-Gaussian populations.

Findings

Corrected tests maintain nominal size in high dimensions.

Traditional chi-square approximations fail in large p.

Corrections are effective for non-Gaussian data.

Abstract

In this paper, we give an explanation to the failure of two likelihood ratio procedures for testing about covariance matrices from Gaussian populations when the dimension is large compared to the sample size. Next, using recent central limit theorems for linear spectral statistics of sample covariance matrices and of random F-matrices, we propose necessary corrections for these LR tests to cope with high-dimensional effects. The asymptotic distributions of these corrected tests under the null are given. Simulations demonstrate that the corrected LR tests yield a realized size close to nominal level for both moderate p (around 20) and high dimension, while the traditional LR tests with chi-square approximation fails. Another contribution from the paper is that for testing the equality between two covariance matrices, the proposed correction applies equally for non-Gaussian populations yielding a valid pseudo-likelihood ratio test.