A high-dimension two-sample test for the mean using cluster subspaces

DOI10.1016/j.csda.2015.12.004zbMath1468.62227OpenAlexW2205780429MaRDI QIDQ1659362

Jie Zhang, Meng Pan

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2015.12.004

zbMATH Keywords

hierarchical clusteringtwo-sample problemHotelling's testhigh-dimension data

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Hypothesis testing in multivariate analysis (62H15)

Related Items (10)

Hotelling's \(T^2\) in separable Hilbert spaces ⋮ High-dimensional general linear hypothesis testing under heteroscedasticity ⋮ High-Dimensional MANOVA Via Bootstrapping and Its Application to Functional and Sparse Count Data ⋮ Distribution and correlation-free two-sample test of high-dimensional means ⋮ Two-sample test in high dimensions through random selection ⋮ Linear hypothesis testing in high-dimensional one-way MANOVA ⋮ Robust two-sample test of high-dimensional mean vectors under dependence ⋮ A review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices ⋮ Integrated rank-weighted depth ⋮ Recent developments in high-dimensional inference for multivariate data: parametric, semiparametric and nonparametric approaches

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• Two-Sample Test of High Dimensional Means Under Dependence
• Multivariate Theory for Analyzing High Dimensional Data
• A Significance Test for the Separation of Two Highly Multivariate Small Samples
• A High Dimensional Two Sample Significance Test

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