Location‐invariant Multi‐sample U‐tests for Covariance Matrices with Large Dimension

DOI10.1111/sjos.12262zbMath1422.62204OpenAlexW2583356962MaRDI QIDQ5738838

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Publication date: 13 June 2017

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/sjos.12262

zbMATH Keywords

limiting distributions\(U\)-statisticsmultivariate distributionshigh-dimensional inferencecovariance matricesmulti-sample sphericity

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (9)

A significance test of the RV coefficient in high dimensions ⋮ On the distribution of the \(\operatorname{T}^2\) statistic, used in statistical process monitoring, for high-dimensional data ⋮ A \(U\)-classifier for high-dimensional data under non-normality ⋮ A unified approach to testing mean vectors with large dimensions ⋮ Multiple comparisons of mean vectors with large dimension under general conditions ⋮ Some correlation tests for vectors of large dimension ⋮ Tests of zero correlation using modified RV coefficient for high-dimensional vectors ⋮ Location-invariant tests of homogeneity of large-dimensional covariance matrices ⋮ Tests for proportionality of matrices with large dimension

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