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

Yujun Wu*, Marc Genton, Leonard A. Stefanski

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


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)
Issue number3
StatePublished - Sep 1 2006


  • Drug discovery
  • High-dimensional data
  • Hotelling's T
  • Small n large p

ASJC Scopus subject areas

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


Dive into the research topics of 'A multivariate two-sample mean test for small sample size and missing data'. Together they form a unique fingerprint.

Cite this