A new test of independence for high-dimensional data

DOI10.1016/j.spl.2014.05.024zbMath1400.62122OpenAlexW2093798753MaRDI QIDQ395953

Guangyu Mao

Publication date: 8 August 2014

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2014.05.024

zbMATH Keywords

independence testhigh dimension

Mathematics Subject Classification ID

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

Related Items (12)

On Schott's and Mao's test statistics for independence of normal random vectors ⋮ On high-dimensional tests for mutual independence based on Pearson’s correlation coefficient ⋮ Testing independence in high dimensions using Kendall's tau ⋮ Empirical likelihood method for complete independence test on high-dimensional data ⋮ High-dimensional asymptotic expansion of the null distribution for Schott’s test statistic for complete independence of normal random variables ⋮ A note on testing complete independence for high dimensional data ⋮ Max-sum test based on Spearman's footrule for high-dimensional independence tests ⋮ Logarithmic law of large random correlation matrices ⋮ Testing independence in high dimensions with sums of rank correlations ⋮ Testing independence in high-dimensional multivariate normal data ⋮ Generalized Schott type tests for complete independence in high dimensions ⋮ Nonparametric tests of independence based on interpoint distances

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• Dependent central limit theorems and invariance principles
• Testing for complete independence in high dimensions
• The true characteristic function of the F distribution
• Bayesian Models for Gene Expression With DNA Microarray Data
• Multivariate Statistics

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