Learning false discovery rates by fitting sigmoidal threshold functions

Details Learning false discovery rates by fitting sigmoidal threshold functions Learning false discovery rates by fitting sigmoidal threshold functions

2011-04-28

arxiv.org/abs/1104.5414v1

Journal de la Soci\'et\'e Fran\c{c}aise de Statistique 2011, Vol. 152, Issue 2, pp. 39-50

Statistics

Methodology

13 pages, 3 figures, 1 table

Scientific paper

False discovery rates (FDR) are typically estimated from a mixture of a null and an alternative distribution. Here, we study a complementary approach proposed by Rice and Spiegelhalter (2008) that uses as primary quantities the null model and a parametric family for the local false discovery rate. Specifically, we consider the half-normal decay and the beta-uniform mixture models as FDR threshold functions. Using simulations and analysis of real data we compare the performance of the Rice-Spiegelhalter approach with that of competing FDR estimation procedures. If the alternative model is misspecified and an empirical null distribution is employed the accuracy of FDR estimation degrades substantially. Hence, while being a very elegant formalism, the FDR threshold approach requires special care in actual application.

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