CENTRAL LIMIT THEOREMS FOR CLASSICAL LIKELIHOOD RATIO TESTS FOR HIGH-DIMENSIONAL NORMAL DISTRIBUTIONS

• 成果类型: Article
• 署名作者: Jiang, Tiefeng; Yang, Fan
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/13-AOS1134
• 发表日期: 2013
• 页码: 2029-2074
• 关键词: Covariance matrices; asymptotic-distribution; largest entries; EQUALITY; INDEPENDENCE; Unbiasedness; sphericity; coherence

摘要

For random samples of size n obtained from p-variate normal distributions, we consider the classical likelihood ratio tests (LRT) for their means and covariance matrices in the high-dimensional setting. These test statistics have been extensively studied in multivariate analysis, and their limiting distributions under the null hypothesis were proved to be chi-square distributions as n goes to infinity and p remains fixed. In this paper, we consider the high-dimensional case where both p and n go to infinity with p/n -> y is an element of (0, 1]. We prove that the likelihood ratio test statistics under this assumption will converge in distribution to normal distributions with explicit means and variances. We perform the simulation study to show that the likelihood ratio tests using our central limit theorems outperform those using the traditional chi-square approximations for analyzing high-dimensional data.