Multiple permutation test for high-dimensional data: a components-combined algorithm

DOI10.1080/00949655.2019.1571058OpenAlexW2911343711MaRDI QIDQ5107348

Wei Yu, Li Xing Zhu, Wang-li Xu

Publication date: 27 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2019.1571058

zbMATH Keywords

type I error ratecomponents-combined algorithmcorrelation matrices testingmean testingmultiple permutation testing

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Recent developments in high-dimensional inference for multivariate data: parametric, semiparametric and nonparametric approaches

Cites Work

• Multivariate and multiple permutation tests
• A combined \(p\)-value test for multiple hypothesis testing
• Permutation \(p\)-values should never be zero: calculating exact \(p\)-values when permutations are randomly drawn
• A two-sample test for high-dimensional data with applications to gene-set testing
• A double generalized Pareto distribution
• An improved Bonferroni procedure for multiple tests of significance
• Two-Sample Test of High Dimensional Means Under Dependence
• A Two-Sample Test for Equality of Means in High Dimension
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