Jump to ContentJump to Main Navigation
Show Summary Details

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

6 Issues per year

IMPACT FACTOR increased in 2015: 1.265
5-year IMPACT FACTOR: 1.423
Rank 42 out of 123 in category Statistics & Probability in the 2015 Thomson Reuters Journal Citation Report/Science Edition

SCImago Journal Rank (SJR) 2015: 0.954
Source Normalized Impact per Paper (SNIP) 2015: 0.554
Impact per Publication (IPP) 2015: 1.061

Mathematical Citation Quotient (MCQ) 2015: 0.06

See all formats and pricing

30,00 € / $42.00 / £23.00

Get Access to Full Text

A Mixture-Model Approach for Parallel Testing for Unequal Variances

Haim Y. Bar
  • Cornell University
/ James G. Booth
  • Cornell University
/ Martin T. Wells
  • Cornell University
Published Online: 2012-01-06 | DOI: https://doi.org/10.2202/1544-6115.1762

Testing for unequal variances is usually performed in order to check the validity of the assumptions that underlie standard tests for differences between means (the t-test and anova). However, existing methods for testing for unequal variances (Levene's test and Bartlett's test) are notoriously non-robust to normality assumptions, especially for small sample sizes. Moreover, although these methods were designed to deal with one hypothesis at a time, modern applications (such as to microarrays and fMRI experiments) often involve parallel testing over a large number of levels (genes or voxels). Moreover, in these settings a shift in variance may be biologically relevant, perhaps even more so than a change in the mean. This paper proposes a parsimonious model for parallel testing of the equal variance hypothesis. It is designed to work well when the number of tests is large; typically much larger than the sample sizes. The tests are implemented using an empirical Bayes estimation procedure which `borrows information' across levels. The method is shown to be quite robust to deviations from normality, and to substantially increase the power to detect differences in variance over the more traditional approaches even when the normality assumption is valid.

Keywords: empirical Bayes; EM algorithm; shrinkage estimation; false discovery rate; mixture model; simultaneous tests

Published Online: 2012-01-06

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 11, Issue 1, Pages 1–21, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1762, January 2012

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Babak Shahbaba and Wesley O. Johnson
Statistics in Medicine, 2013, Volume 32, Number 12, Page 2114

Comments (0)

Please log in or register to comment.