Some tests for the equality of covariance matrices

DOI10.1016/S0378-3758(00)00209-3zbMath0971.62031WikidataQ126351360 ScholiaQ126351360MaRDI QIDQ5932164

James R. Schott

Publication date: 4 October 2001

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

zbMATH Keywords

kurtosiselliptical distributionsmultivariate normal populationssimulationsWald statistic

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15)

Related Items (17)

Empirical likelihood test for the equality of several high-dimensional covariance matrices ⋮ On Schott's and Mao's test statistics for independence of normal random vectors ⋮ Likelihood Ratio Tests for High‐Dimensional Normal Distributions ⋮ Testing Independence via Spectral Moments ⋮ Robust asymptotic tests for the equality of multivariate coefficients of variation ⋮ Reduced-rank estimation of the difference between two covariance matrices ⋮ Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions ⋮ A note on the fourth cumulant of a finite mixture distribution ⋮ Preliminary Multiple-Test Estimation, With Applications to k-Sample Covariance Estimation ⋮ Limiting distributions of the likelihood ratio test statistics for independence of normal random vectors ⋮ A study of two high-dimensional likelihood ratio tests under alternative hypotheses ⋮ Optimal rank-based tests for homogeneity of scatter ⋮ Generalized Schott type tests for complete independence in high dimensions ⋮ Optimal tests for homogeneity of covariance, scale, and shape ⋮ A test for the equality of covariance matrices when the dimension is large relative to the sample sizes ⋮ Testing equality of covariance matrices when data are incomplete ⋮ Limiting distributions of likelihood ratio test for independence of components for high-dimensional normal vectors

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Robustness and efficiency properties of scatter matrices
• Testing the equality of variance-covariance matrices the robust way
• Asymptotic distributions in canonical correlation analysis and other multivariate procedures for nonnormal populations
• Between-Groups Comparison of Principal Components
• Some tests for common principal component subspaces in several groups
• Bootstrap Critical Values for Testing Homogeneity of Covariance Matrices
• Comparative Robustness of Six Tests in Multivariate Analysis of Variance
• Generalized Inverses, Wald's Method, and the Construction of Chi-Squared Tests of Fit

This page was built for publication: Some tests for the equality of covariance matrices