A General Framework for Multiple Testing Dependence

Tuesday, January 27, 2009 
04:30 PM in LILY G126
Professor John Storey
Department of Molecular Biology, Princeton University

I will present a general framework for performing large-scale significance testing in the presence of arbitrarily strong dependence. We have derived a low-dimensional set of random vectors, called a dependence kernel, that fully captures the dependence structure in an observed high-dimensional data set. This result shows a surprising reversal of the "curse of dimensionality" in the high-dimensional hypothesis testing setting. We have also shown theoretically that conditioning on a dependence kernel is sufficient to render statistical tests independent regardless of the level of dependence in the observed data. Finally, I demonstrate that this framework for multiple testing dependence has implications in a variety of common multiple testing problems, such as in gene expression studies, brain imaging, and spatial epidemiology. This is joint work with Jeffrey Leek.

Associated reading:

Leek JT and Storey JD (2008), A general framework for multiple testing dependence. PNAS, 105: 18718-18723.

Last Updated: Aug 31, 2017 2:54 PM

Purdue Department of Statistics, 250 N. University St, West Lafayette, IN 47907

Phone: (765) 494-6030, Fax: (765) 494-0558

© 2015 Purdue University | An equal access/equal opportunity university | Copyright Complaints

Trouble with this page? Disability-related accessibility issue? Please contact the College of Science Webmaster.