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

Wei Yu

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 13, 4476-4486

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.

Date: 2022
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DOI: 10.1080/03610926.2020.1815782

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