School Seminars and Colloquia

Adaptive procedures for false discovery rate estimation

School Seminar

by Dr Kun Liang


Institution: Dept of Statistics, University of Wisconsin-Madison
Date: Wed 22nd February 2012
Time: 11:00 AM
Location: Russell Love Theatre, Richard Berry Building, The University of Melbourne

Abstract: Multiple testing has generated a surging interest in recent years due to the wide availability of large and complex modern data sets. Much research focused on the false discovery rate (FDR) estimation and control, and adaptive procedures have particularly attracted growing attention. By incorporating good estimates of the proportion of true null hypotheses among all hypotheses, adaptive procedures have been shown to increase the power of detecting non-null hypotheses while maintaining the FDR. Most existing adaptive procedures rely on tuning parameters, which can be either assigned a priori (fixed) or estimated from data (dynamically). In this talk, I will first provide a finite sample proof of conservative point estimation for fixed adaptive FDR procedures. Then, I will present a general condition under which dynamic adaptive procedures can lead to conservative null proportion and FDR estimators. Simulation results show that a novel dynamic adaptive procedure achieves more power through smaller estimation errors for null proportion under independence and mild dependence conditions.

For More Information: contact: Prof Richard Huggins. email: rhuggins@unimelb.edu.au