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

Yujun Wu; Marc Genton; Leonard A. Stefanski

doi:10.1111/j.1541-0420.2006.00533.x

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

Computer, Electrical and Mathematical Sciences and Engineering

Research output: Contribution to journal › Article › peer-review

25 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 language	English (US)
Journal	Biometrics
Volume	62
Issue number	3
DOIs	https://doi.org/10.1111/j.1541-0420.2006.00533.x
State	Published - Sep 1 2006

Keywords

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

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10.1111/j.1541-0420.2006.00533.x

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N2 - 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.

AB - 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.

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

Abstract

Keywords

ASJC Scopus subject areas

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