Please use this identifier to cite or link to this item:
Title: Corrections to LRT on large-dimensional covariance matrix by RMT
Authors: Bai, Z. 
Jiang, D.
Yao, J.-F.
Zheng, S.
Keywords: High-dimensional data
Marčenko-pastur distributions
Random F-matrices
Testing on covariance matrices
Issue Date: Dec-2009
Citation: Bai, Z., Jiang, D., Yao, J.-F., Zheng, S. (2009-12). Corrections to LRT on large-dimensional covariance matrix by RMT. Annals of Statistics 37 (6 B) : 3822-3840. ScholarBank@NUS Repository.
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 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 χ 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. © Institute of Mathematical Statistics, 2009.
Source Title: Annals of Statistics
ISSN: 00905364
DOI: 10.1214/09-AOS694
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Oct 3, 2022


checked on Oct 3, 2022

Page view(s)

checked on Sep 22, 2022

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.