Two-sample tests for high-dimensional covariance matrices using both difference and ratio

DOI10.1214/20-EJS1783zbMath1459.62088OpenAlexW3119444354MaRDI QIDQ2219224

Ruitao Lin, Guo-Liang Tian, Tingting Zou, Shurong Zheng

Publication date: 19 January 2021

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1609902188

zbMATH Keywords

asymptotic normalityrandom matrix theoryhigh-dimensional covariance matricespower enhancement

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Hypothesis testing in multivariate analysis (62H15) Random matrices (probabilistic aspects) (60B20) Probability distributions: general theory (60E05)

Cites Work

• Unnamed Item
• A test for the equality of covariance matrices when the dimension is large relative to the sample sizes
• Two sample tests for high-dimensional covariance matrices
• Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions
• Testing the equality of several covariance matrices with fewer observations than the dimension
• Corrections to LRT on large-dimensional covariance matrix by RMT
• Unbiasedness of the likelihood ratio tests for equality of several covariance matrices and equality of several multivariate normal populations
• No eigenvalues outside the support of the limiting spectral distribution of large-dimensional sample covariance matrices
• Properties of tests concerning covariance matrices of normal distributions
• CLT for linear spectral statistics of large-dimensional sample covariance matrices.
• A note on the CLT of the LSS for sample covariance matrix from a spiked population model
• Substitution principle for CLT of linear spectral statistics of high-dimensional sample covariance matrices with applications to hypothesis testing
• Distribution of likelihood ratio statistic for testing equality of covariance matrices of multivariate Gaussian models
• Power Enhancement in High-Dimensional Cross-Sectional Tests
• Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings
• Testing homogeneity of high-dimensional covariance matrices
• Choosing between methods of combining $p$-values
• Unbiasedness of Some Test Criteria for the Equality of One or Two Covariance Matrices

This page was built for publication: Two-sample tests for high-dimensional covariance matrices using both difference and ratio