Testing proportionality of two large-dimensional covariance matrices

DOI10.1016/j.csda.2014.03.014zbMath1506.62197OpenAlexW2076304629MaRDI QIDQ1623621

Lin Xu, Shurong Zheng, Shaokun Bao, Bai-Sen Liu

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2014.03.014

zbMATH Keywords

likelihood ratio testlimiting spectral distributionproportionalitylarge-dimensional datarandom F-matrices

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15) Random matrices (probabilistic aspects) (60B20)

Related Items (6)

High-dimensional proportionality test of two covariance matrices and its application to gene expression data ⋮ High-dimensional testing for proportional covariance matrices ⋮ Testing proportionality of two high-dimensional covariance matrices ⋮ Testing the equality of two high‐dimensional spatial sign covariance matrices ⋮ Limit theorem associated with Wishart matrices with application to hypothesis testing for common principal components ⋮ A high dimensional nonparametric test for proportional covariance matrices

Cites Work

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• Some Distribution Problems Connected with the Characteristic Roots of $S_1S^{-1}_2$
• On the Distributions of the Ratios of the Roots of a Covariance Matrix and Wilks' Criterion for Tests of Three Hypotheses
• Testing Proportionality of Covariance Matrices

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