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

Dandan Jiang

Journal of Multivariate Analysis, 2016, vol. 152, issue C, 28-39

Abstract: 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, which breaks the inherent idea that the corrected tests by RMT can only be used when pKeywords: Large-dimensional data; Covariance structure; Rao’s score test; Random matrix theory (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1016/j.jmva.2016.07.010

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