A High-Dimensional Nonparametric Multivariate Test for Mean Vector

Lan Wang, Bo Peng, Runze Li

Research output: Contribution to journalArticle

41 Scopus citations

Abstract

This work is concerned with testing the population mean vector of nonnormal high-dimensional multivariate data. Several tests for high-dimensional mean vector, based on modifying the classical Hotelling T2 test, have been proposed in the literature. Despite their usefulness, they tend to have unsatisfactory power performance for heavy-tailed multivariate data, which frequently arise in genomics and quantitative finance. This article proposes a novel high-dimensional nonparametric test for the population mean vector for a general class of multivariate distributions. With the aid of new tools in modern probability theory, we proved that the limiting null distribution of the proposed test is normal under mild conditions when p is substantially larger than n. We further study the local power of the proposed test and compare its relative efficiency with a modified Hotelling T2 test for high-dimensional data. An interesting finding is that the newly proposed test can have even more substantial power gain with large p than the traditional nonparametric multivariate test does with finite fixed p. We study the finite sample performance of the proposed test via Monte Carlo simulations. We further illustrate its application by an empirical analysis of a genomics dataset. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1658-1669
Number of pages12
JournalJournal of the American Statistical Association
Volume110
Issue number512
DOIs
StatePublished - Oct 2 2015

Keywords

  • Asymptotic relative efficiency
  • High-dimensional multivariate data
  • Hotelling T test
  • Nonparametric multivariate test

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