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A new method for multi-sample high-dimensional covariance matrices test based on permutation

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  • Wei Yu

Abstract

For multi-sample covariance testing, the classical likelihood ratio test is often efficient and powerful in low-dimensional normal cases. However, when the dimension is larger than the sample size, it fails to work in practice and theory. This paper proposes a permutation based test to handle high-dimensional covariance testing problem with more than two samples. Numerical studies show that the test controls type I error rate well for both normal and non-normal data. The power performance is also competitive with existing methods. In addition, a real data example of DNA microarray is analyzed for illustration.

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  • Wei Yu, 2022. "A new method for multi-sample high-dimensional covariance matrices test based on permutation," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(13), pages 4476-4486, June.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:13:p:4476-4486
    DOI: 10.1080/03610926.2020.1815782
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