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

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  • Jiang, Dandan

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 p

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  • Jiang, Dandan, 2016. "Tests for large-dimensional covariance structure based on Rao’s score test," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 28-39.
    • Handle: RePEc:eee:jmvana:v:152:y:2016:i:c:p:28-39
    DOI: 10.1016/j.jmva.2016.07.010
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    1. Chen, Song Xi & Zhang, Li-Xin & Zhong, Ping-Shou, 2010. "Tests for High-Dimensional Covariance Matrices," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 810-819.
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    Cited by:

    1. Jiang, Hui & Wang, Shaochen, 2017. "Moderate deviation principles for classical likelihood ratio tests of high-dimensional normal distributions," Journal of Multivariate Analysis, Elsevier, vol. 156(C), pages 57-69.
    2. Klein, Daniel & Pielaszkiewicz, Jolanta & Filipiak, Katarzyna, 2022. "Approximate normality in testing an exchangeable covariance structure under large- and high-dimensional settings," Journal of Multivariate Analysis, Elsevier, vol. 192(C).

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