A new method for multi-sample high-dimensional covariance matrices test based on permutation. Issue 13 (16th June 2022)

• Record Type: Journal Article
• Authors: Yu, Wei
• Is Part Of: Communications in statistics. Volume 51:Issue 13(2022)
• Page Start: 4476
• Page End: 4486
• Publication Date: 2022-06-16
• Subjects: High-dimensional data -- covariance matrices testing -- permutation test -- type I error rate; 62H15; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2020.1815782
• Languages: English
• ISSNs: 0361-0926

Abstract

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.