Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido

IMPACT FACTOR 2018: 0.536
5-year IMPACT FACTOR: 0.764

CiteScore 2018: 0.49

SCImago Journal Rank (SJR) 2018: 0.316
Source Normalized Impact per Paper (SNIP) 2018: 0.342

Mathematical Citation Quotient (MCQ) 2018: 0.02

See all formats and pricing
More options …
Volume 11, Issue 1


Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

A Mixture-Model Approach for Parallel Testing for Unequal Variances

Haim Y. Bar / James G. Booth / Martin T. Wells
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

About the article

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.

Export Citation

©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

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.

Xuan Li, Yuejiao Fu, Xiaogang Wang, Dawn L. DeMeo, Kelan Tantisira, Scott T. Weiss, and Weiliang Qiu
International Journal of Genomics, 2018, Volume 2018, Page 1
Julia L. Finkelstein, Eva K. Pressman, Elizabeth M. Cooper, Tera R. Kent, Haim Y. Bar, and Kimberly O. O’Brien
Reproductive Sciences, 2015, Volume 22, Number 6, Page 685
Li Ma, Gabriel Hoffman, and Alon Keinan
BMC Genomics, 2015, Volume 16, Number 1
Kun Chen, Neha Mishra, Joan Smyth, Haim Bar, Elizabeth Schifano, Lynn Kuo, and Ming-Hui Chen
Journal of the American Statistical Association, 2017, Page 0
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.
Log in