A multivariate two-sample mean test for small sample size and missing data

Yujun Wu*, Marc G. Genton, Leonard A. Stefanski

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

We develop a new statistic for testing the equality of two multivariate mean vectors. A scaled chi-squared distribution is proposed as an approximating null distribution. Because the test statistic is based on componentwise statistics, it has the advantage over Hotelling's T2 test of being applicable to the case where the dimension of an observation exceeds the number of observations. An appealing feature of the new test is its ability to handle missing data by relying on only componentwise sample moments. Monte Carlo studies indicate good power compared to Hotelling's T2 and a recently proposed test by Srivastava (2004, Technical Report, University of Toronto). The test is applied to drug discovery data.

Original languageEnglish (US)
Pages (from-to)877-885
Number of pages9
JournalBiometrics
Volume62
Issue number3
DOIs
StatePublished - Sep 2006
Externally publishedYes

Keywords

  • Drug discovery
  • High-dimensional data
  • Small n large p

ASJC Scopus subject areas

  • General Immunology and Microbiology
  • Applied Mathematics
  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences
  • Statistics and Probability

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