title: On the geometric modeling approach to empirical null distribution estimation for empirical Bayes modeling of multiple hypothesis testing.
abstract: We study the geometric modeling approach to estimating the null distribution for the empirical Bayes modeling of multiple hypothesis testing. The commonly used method is a nonparametric approach based on the Poisson regression, which however could be unduly affected by the dependence among test statistics and perform very poorly under strong dependence. In this paper, we explore a finite mixture model based geometric modeling approach to empirical null distribution estimation and multiple hypothesis testing. Through simulations and applications to two public microarray data, we will illustrate its competitive performance.
authors: Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA. baolin@umn.edu
authors: Baolin
authors: B
authors: Wu
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D007938:Leukemia / Q000235:genetics; D008297:Male; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D011471:Prostatic Neoplasms / Q000235:genetics