Testing the independence of two random vectors where only one dimension is large

DOI10.1080/02331888.2016.1266988zbMath1369.62123arXiv1504.04935OpenAlexW3102267857MaRDI QIDQ5276174

Jiaqi Chen, Jian-feng Yao, Wei-Ming Li

Publication date: 14 July 2017

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1504.04935

zbMATH Keywords

asymptotic normalitycovariance matrixindependence testgene networkMonte-Carlo studyhigh-dimensional testing

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Hypothesis testing in multivariate analysis (62H15) Protein sequences, DNA sequences (92D20) Paired and multiple comparisons; multiple testing (62J15)

Related Items (7)

The abstract of doctoral dissertation ‘Some research on hypothesis testing and nonparametric variable screening problems for high dimensional data’ ⋮ Independence test in high-dimension using distance correlation and power enhancement technique ⋮ Proposition and validation of multivariate tests of independence between two groups of variables ⋮ Limiting distributions of the likelihood ratio test statistics for independence of normal random vectors ⋮ Block-diagonal test for high-dimensional covariance matrices ⋮ Limiting distributions of likelihood ratio test for independence of components for high-dimensional normal vectors ⋮ Likelihood ratio tests under model misspecification in high dimensions

Cites Work

• On the sphericity test with large-dimensional observations
• Testing the structure of the covariance matrix with fewer observations than the dimension
• Corrections to LRT on large-dimensional covariance matrix by RMT
• Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
• Testing the independence of sets of large-dimensional variables
• Independence test for high dimensional data based on regularized canonical correlation coefficients
• A two-sample test for high-dimensional data with applications to gene-set testing
• On the Independence of k Sets of Normally Distributed Statistical Variables

This page was built for publication: Testing the independence of two random vectors where only one dimension is large