A New Test on High-Dimensional Mean Vector Without Any Assumption on Population Covariance Matrix

DOI10.1080/03610926.2012.717663zbMath1307.62151OpenAlexW2022559372MaRDI QIDQ5177621

Yutaka Kano, Shota Katayama

Publication date: 13 March 2015

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2012.717663

zbMATH Keywords

asymptotic distributionshigh-dimensional datatesting mean vector

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Hypothesis testing in multivariate analysis (62H15)

Related Items (4)

A unified approach to testing mean vectors with large dimensions ⋮ A note on the unbiased estimator of \(\mathbf{\Sigma}^2\) ⋮ Linear hypothesis testing in high-dimensional one-way MANOVA ⋮ Linear hypothesis testing in high-dimensional heteroscedastic one-way MANOVA: a normal reference \(L^2\)-norm based test

Cites Work

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• A test for the mean vector with fewer observations than the dimension under non-normality
• A two-sample test for high-dimensional data with applications to gene-set testing
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Tests for High-Dimensional Covariance Matrices

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