Hypothesis tests for high-dimensional covariance structures

DOI10.1007/s10463-020-00760-5zbMath1469.62274OpenAlexW3046817330MaRDI QIDQ2042528

Makoto Aoshima, Aki Ishii, Kazuyoshi Yata

Publication date: 20 July 2021

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://tsukuba.repo.nii.ac.jp/record/2001767/files/AISM_73-3.pdf

zbMATH Keywords

unbiased estimateHDLSScross-data-matrix methodologyintraclass correlation modeldiagonal structuretest of eigenvector

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Hypothesis testing in multivariate analysis (62H15) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (4)

CORRELATION MATRIX OF EQUI-CORRELATED NORMAL POPULATION: FLUCTUATION OF THE LARGEST EIGENVALUE, SCALING OF THE BULK EIGENVALUES, AND STOCK MARKET ⋮ Likelihood ratio tests for elaborate covariance structures and for MANOVA models with elaborate covariance structures -- a review ⋮ Hypothesis testing on compound symmetric structure of high-dimensional covariance matrix ⋮ Limit theorem associated with Wishart matrices with application to hypothesis testing for common principal components

Cites Work

• Unnamed Item
• High-dimensional inference on covariance structures via the extended cross-data-matrix methodology
• Correlation tests for high-dimensional data using extended cross-data-matrix methodology
• Testing the structure of the covariance matrix with fewer observations than the dimension
• Some tests for the covariance matrix with fewer observations than the dimension under non-normality
• Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations
• Spectral statistics of large dimensional Spearman's rank correlation matrix and its application
• Asymptotic properties of the first principal component and equality tests of covariance matrices in high-dimension, low-sample-size context
• Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix
• Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
• Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models
• Tests for covariance structures with high-dimensional repeated measurements
• Equality tests of high-dimensional covariance matrices under the strongly spiked eigenvalue model
• A two-sample test for high-dimensional data with applications to gene-set testing
• Asymptotic normality for inference on multisample, high-dimensional mean vectors under mild conditions
• Testing for complete independence in high dimensions
• Two-sample tests for high-dimension, strongly spiked eigenvalue models
• A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise
• Tests for High-Dimensional Covariance Matrices

This page was built for publication: Hypothesis tests for high-dimensional covariance structures