Tests of zero correlation using modified RV coefficient for high-dimensional vectors

DOI10.1007/s42519-019-0043-xzbMath1426.62173OpenAlexW2944465948WikidataQ127909795 ScholiaQ127909795MaRDI QIDQ2321773

Yanyan Li

Publication date: 23 August 2019

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s42519-019-0043-x

zbMATH Keywords

block-diagonal structurehigh-dimensional inferencecross-correlations

Mathematics Subject Classification ID

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

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On testing sphericity and identity of a covariance matrix with large dimensions
• Test of independence for functional data
• The distance correlation \(t\)-test of independence in high dimension
• Correlation tests for high-dimensional data using extended cross-data-matrix methodology
• Bootstrap and permutation tests of independence for point processes
• A test for independence of two sets of variables when the number of variables is large relative to the sample size
• Matrix correlation
• Some results on vector correlation
• Cramer-von Mises tests for independence
• A procedure for assessing vector correlations
• Asymptotic theory for canonical correlation analysis
• A significance test of the RV coefficient in high dimensions
• Asymptotic theory of multiple-set linear canonical analysis
• A unified approach to testing mean vectors with large dimensions
• Location-invariant tests of homogeneity of large-dimensional covariance matrices
• Independence test for high dimensional data based on regularized canonical correlation coefficients
• A necessary test for complete independence in high dimensions using rank-correlations
• A multivariate nonparametric test of independence
• Approximation Theorems of Mathematical Statistics
• Asymptotic distributions in canonical correlation analysis and other multivariate procedures for nonnormal populations
• Asymptotic Statistics
• Kernel-Based Tests for Joint Independence
• Algorithmic Learning Theory
• Canonical analysis of several sets of variables
• Location‐invariant Multi‐sample U‐tests for Covariance Matrices with Large Dimension

This page was built for publication: Tests of zero correlation using modified RV coefficient for high-dimensional vectors