 Two-sample high dimensional mean test based on prepivotsSantu Ghosh, Deepak Nag Ayyala and Rafael Hellebuyck Computational Statistics & Data Analysis, 2021, vol. 163, issue C Abstract: Testing equality of mean vectors is a very commonly used criterion when comparing two multivariate random variables. Traditional tests such as Hotelling's T2 become either unusable or output small power when the number of variables is greater than the combined sample size. A novel method is proposed using both prepivoting and Edgeworth expansion for testing the equality of two population mean vectors in a “large p, small n” setting. The asymptotic null distribution of the test statistic is derived and it is shown that the power of suggested test converges to one under certain alternatives when both n and p increase to infinity. Finite sample performance of the proposed test statistic is compared with other recently developed tests designed to also handle the “large p, small n” situation through simulations. The proposed test achieves competitive rates for both type I error rate and power. The usefulness of suggested test is illustrated by applications to two microarray gene expression data sets. Keywords: High dimensional; Mean vector test; Prepivoting; Edgeworth expansion; Dense and sparse alternatives (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:163:y:2021:i:c:s0167947321001183 DOI: 10.1016/j.csda.2021.107284 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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