An RIHT statistic for testing the equality of several high-dimensional mean vectors under homoskedasticity

DOI10.1016/j.csda.2023.107855MaRDI QIDQ6071712

No author found.

Publication date: 28 November 2023

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

zbMATH Keywords

central limit theoremhigh-dimensional data analysismean vector testexact four-moment theorem

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Cites Work

• Unnamed Item
• Unnamed Item
• An adaptable generalization of Hotelling's $T^2$ test in high dimension
• Random covariance matrices: universality of local statistics of eigenvalues
• On testing the equality of high dimensional mean vectors with unequal covariance matrices
• Shrinkage-based regularization tests for high-dimensional data with application to gene set analysis
• Spectral analysis of large dimensional random matrices
• A generalized likelihood ratio test for normal mean when \(p\) is greater than \(n\)
• An adaptive test for the mean vector in large-\(p\)-small-\(n\) problems
• On the distribution of the largest eigenvalue in principal components analysis
• CLT for linear spectral statistics of large-dimensional sample covariance matrices.
• Tests for multivariate analysis of variance in high dimension under non-normality
• A two-sample test for high-dimensional data with applications to gene-set testing
• Some high-dimensional tests for a one-way MANOVA
• A Regularized Hotelling’sT²Test for Pathway Analysis in Proteomic Studies
• Asymptotic Results of a High Dimensional MANOVA Test and Power Comparison When the Dimension is Large Compared to the Sample Size
• Two-Sample Test of High Dimensional Means Under Dependence
• Ridgelized Hotelling’s T² test on mean vectors of large dimension