Statistics and Data Science Seminar
Prof. Wei Biao Wu
University of Chicago
On False Discovery Control under Dependence
Abstract: A popular framework for false discovery control is the random effects model in which the null hypotheses are assumed to be independent. I will generalize this random effects model to a conditional dependence model which allows dependence between null hypotheses. The dependence can be useful to characterize the spatial structure of the null hypotheses. Asymptotic properties of false discovery proportions and numbers of rejected hypotheses are explored and a large-sample distributional theory is obtained.
The talk is based on the paper: Wu, W. B. (2008) On false discovery control under dependence, Ann. Statist. 36, 364--380.
Wednesday January 28, 2009 at 4:15 PM in SEO 612