Tests for large-dimensional covariance structure based on Rao’s score test

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• 作者：Dandan Jiang ^{jiangdandan@jlu.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：62H15 ; 62H10
• 刊名：Journal of Multivariate Analysis
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：152
• 期：Complete
• 页码：28-39
• 全文大小：661 K

文摘

This paper proposes a new test for covariance matrices based on the correction to Rao’s score test in a large-dimension framework. By generalizing the corresponding CLT for linear spectral statistics, the test can be made applicable for large-dimension non-Gaussian variables in a wider range without the 4th-moment restriction. Moreover, the proposed corrected Rao’s score test (CRST) remains powerful even when p≫n$p\gg n$, which breaks the inherent idea that the corrected tests by RMT can only be used when p<n$p<n$. Finally, we compare the proposed test with other high-dimension covariance structure tests to evaluate their performances through a simulation study.