CORRECTIONS TO LRT ON LARGE-DIMENSIONAL COVARIANCE MATRIX BY RMT
成果类型：
Article
署名作者：
Bai, Zhidong; Jiang, Dandan; Yao, Jian-Feng; Zheng, Shurong
署名单位：
Northeast Normal University - China; National University of Singapore; Northeast Normal University - China; Universite de Rennes; Universite de Rennes
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
DOI：
10.1214/09-AOS694
发表日期：
2009
页码：
3822-3840
关键词：
摘要：
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 chi(2) 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.