Hypothesis tests for high-dimensional covariance structures
type: Item
instance of: scholarly article
title: Hypothesis tests for high-dimensional covariance structures
zbMATH Open document ID: 1469.62274
author: Aki Ishii
author: Kazuyoshi Yata
author: Makoto Aoshima
published in: Annals of the Institute of Statistical Mathematics
publication date: 2021-07-20T00:00:00Z
full work available at URL: AISM 73-3.pdf
Mathematics Subject Classification ID: 62H15
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 62H20
zbMATH DE Number: 7373099
zbMATH Keywords: cross-data-matrix methodology
zbMATH Keywords: diagonal structure
zbMATH Keywords: HDLSS
zbMATH Keywords: intraclass correlation model
zbMATH Keywords: test of eigenvector
zbMATH Keywords: unbiased estimate
MaRDI profile type: Publication
OpenAlex ID: W3046817330
cites work: Asymptotic normality for inference on multisample, high-dimensional mean vectors under mild conditions
cites work: Two-sample tests for high-dimension, strongly spiked eigenvalue models
cites work: Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models
cites work: Q4882268
cites work: Spectral statistics of large dimensional Spearman's rank correlation matrix and its application
cites work: A two-sample test for high-dimensional data with applications to gene-set testing
cites work: Tests for High-Dimensional Covariance Matrices
cites work: Asymptotic properties of the first principal component and equality tests of covariance matrices in high-dimension, low-sample-size context
cites work: Equality tests of high-dimensional covariance matrices under the strongly spiked eigenvalue model
cites work: Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
cites work: Testing for complete independence in high dimensions
cites work: Some tests for the covariance matrix with fewer observations than the dimension under non-normality
cites work: Testing the structure of the covariance matrix with fewer observations than the dimension
cites work: Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix
cites work: Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations
cites work: Correlation tests for high-dimensional data using extended cross-data-matrix methodology
cites work: High-dimensional inference on covariance structures via the extended cross-data-matrix methodology
cites work: A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise
cites work: Tests for covariance structures with high-dimensional repeated measurements
DOI: 10.1007/S10463-020-00760-5
description: scientific article; zbMATH DE number 7373099