A generalized likelihood ratio test for linear hypothesis of k -sample means in high dimension

DOI10.1080/03610926.2022.2069820OpenAlexW4225289307MaRDI QIDQ6089135

Shiting Liang, Ming Xiang Cao

Publication date: 17 November 2023

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

Full work available at URL: https://doi.org/10.1080/03610926.2022.2069820

zbMATH Keywords

high-dimensional datalarge \(p\) small \(n\)normal modellinear hypothesisgeneralized likelihood ratio method

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15)

Cites Work

• Unnamed Item
• Unnamed Item
• Testing linear hypotheses of mean vectors for high-dimension data with unequal covariance matrices
• Linear hypothesis testing in high-dimensional one-way MANOVA
• A generalized likelihood ratio test for normal mean when \(p\) is greater than \(n\)
• Test on the linear combinations of mean vectors in high-dimensional data
• High-dimensional general linear hypothesis testing under heteroscedasticity
• Note on a solution of the generalized Behrens-Fisher problem
• A High Dimensional Two Sample Significance Test
• On a Heuristic Method of Test Construction and its use in Multivariate Analysis
• A test on linear hypothesis of \(k\)-sample means in high-dimensional data

This page was built for publication: A generalized likelihood ratio test for linear hypothesis of k -sample means in high dimension