Homogeneity test of several high-dimensional covariance matrices for stationary processes under non-normality

DOI10.1080/03610926.2021.1960375arXiv2008.09259OpenAlexW3188367740MaRDI QIDQ6106231

Dong Han, Unnamed Author

Publication date: 27 June 2023

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2008.09259

zbMATH Keywords

high-dimensional datanon-normal distributiondependent stationary processhomogeneity of several covariance matricesmodified box \(M\) test

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• A test for the equality of covariance matrices when the dimension is large relative to the sample sizes
• More powerful tests for sparse high-dimensional covariances matrices
• Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions
• Likelihood ratio tests for covariance matrices of high-dimensional normal distributions
• Test for bandedness of high-dimensional covariance matrices and bandwidth estimation
• Testing the equality of several covariance matrices with fewer observations than the dimension
• Strong approximation for a class of stationary processes
• Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
• Modified Pillai's trace statistics for two high-dimensional sample covariance matrices
• Equality tests of high-dimensional covariance matrices under the strongly spiked eigenvalue model
• A \(U\)-statistic approach for a high-dimensional two-sample mean testing problem under non-normality and Behrens-Fisher setting
• Optimal hypothesis testing for high dimensional covariance matrices
• Komlós-Major-Tusnády approximation under dependence
• Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings
• Testing homogeneity of several covariance matrices and multi-sample sphericity for high-dimensional data under non-normality
• Tests for High-Dimensional Covariance Matrices
• Establishing the Positive Definiteness of the Sample Covariance Matrix
• Properties of sufficiency and statistical tests
• A GENERAL DISTRIBUTION THEORY FOR A CLASS OF LIKELIHOOD CRITERIA
• Homogeneity tests of covariance matrices with high-dimensional longitudinal data

This page was built for publication: Homogeneity test of several high-dimensional covariance matrices for stationary processes under non-normality