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

Learning false discovery rates by fitting sigmoidal threshold functions