Department of
presents
Dr. Michael Kosorok
ABSTRACT
The "large p, small n" paradigm arises in microarray studies, image analysis, high throughput molecular screening, astronomy, and in many other high dimensional applications. False discovery rate (FDR) methods are useful for resolving the accompanying multiple testing problems. In cDNA microarray studies, for example, p-values may be computed for each of p genes using data from n arrays, where p is in the thousands and n is typically less than 30. For FDR methods to be valid in identifying differentially expressed genes, the p-values for the non-differentially expressed genes must simultaneously have uniform distributions marginally. While feasible for permutation p-values, this uniformity is problematic for asymptotic based p-values since the number of p-values involved goes to infinity and intuition suggests that at least some of the p-values should behave erratically. We examine this neglected issue when n is moderate but p is exponentially larger than n. We show the somewhat surprising result that, under very general dependency structures and for a variety of test procedures, the p-values are simultaneously valid. We demonstrate the results with simulation studies and an example where the p-values are computed from a robust, least-absolute deviation procedure.
This work is joint work with Shuangge Ma at Yale University.
Friday, October 27, 2006
3:35 - 4:35 pm
206 Cox Hall
Refreshments will be served on the second floor of Dabney Hall (left of Room 222) at 3:00 pm.