Open Access
December 2009 Corrections to LRT on large-dimensional covariance matrix by RMT
Zhidong Bai, Dandan Jiang, Jian-Feng Yao, Shurong Zheng
Ann. Statist. 37(6B): 3822-3840 (December 2009). DOI: 10.1214/09-AOS694


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 p is large compared to the sample size n. 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 χ2 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.


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Zhidong Bai. Dandan Jiang. Jian-Feng Yao. Shurong Zheng. "Corrections to LRT on large-dimensional covariance matrix by RMT." Ann. Statist. 37 (6B) 3822 - 3840, December 2009.


Published: December 2009
First available in Project Euclid: 23 October 2009

zbMATH: 1360.62286
MathSciNet: MR2572444
Digital Object Identifier: 10.1214/09-AOS694

Primary: 62H15
Secondary: 62H10

Keywords: High-dimensional data , Marčenko–Pastur distributions , random F-matrices , testing on covariance matrices

Rights: Copyright © 2009 Institute of Mathematical Statistics

Vol.37 • No. 6B • December 2009
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