Distribution approximation of covariance matrix eigenvalues

In multivariate analysis, the eigenvalues of the covariance matrix are crucial. Thus, there is a demand among users to find a good, easy-to-use chi-squared approximation. However, there are few good approximations for eigenvalues. Therefore, in this paper, we focus on the chi-squared approximation,...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 52; no. 9; pp. 4313 - 4325
Main Authors Tsukada, Shin-ichi, Sugiyama, Takatoshi
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.09.2023
Taylor & Francis Ltd
Subjects
Approximation
Chi-square test
Chi-squared approximation
Covariance matrix
Eigenvalues
Mathematical analysis
Multivariate analysis
Principal component analysis
Wishart matrix
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ISSN0361-0918
1532-4141
DOI10.1080/03610918.2021.1960998

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Summary:In multivariate analysis, the eigenvalues of the covariance matrix are crucial. Thus, there is a demand among users to find a good, easy-to-use chi-squared approximation. However, there are few good approximations for eigenvalues. Therefore, in this paper, we focus on the chi-squared approximation, proposing a new approximation and investigating its accuracy.
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ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2021.1960998