On the geometric modeling approach to empirical null distribution estimation for empirical Bayes modeling of multiple hypothesis testing

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.

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► A flexible modeling approach to estimating empirical null distribution for appropriate control of false positives.
► Detailed simulation studies demonstrating the very competitive performance of the proposed method.
► Applications to two microarray data illustrating the favorable performance of the proposed method.