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

Paper ID Volume ID Publish Year Pages File Format Full-Text
15150 1382 2013 6 PDF Available
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.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► 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.

Keywords
Empirical Bayes modeling; Empirical null distribution; False discovery rate; Finite mixture model; Multiple hypotheses testing
First Page Preview
On the geometric modeling approach to empirical null distribution estimation for empirical Bayes modeling of multiple hypothesis testing
Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 43, April 2013, Pages 17–22
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering