False discovery rates: a new deal.

ABSTRACT: We introduce a new Empirical Bayes approach for large-scale hypothesis testing, including estimating false discovery rates (FDRs), and effect sizes. This approach has two key differences from existing approaches to FDR analysis. First, it assumes that the distribution of the actual (unobserved) effects is unimodal, with a mode at 0. This "unimodal assumption" (UA), although natural in many contexts, is not usually incorporated into standard FDR analysis, and we demonstrate how incorporating it brings many benefits. Specifically, the UA facilitates efficient and robust computation-estimating the unimodal distribution involves solving a simple convex optimization problem-and enables more accurate inferences provided that it holds. Second, the method takes as its input two numbers for each te

SUBMITTER: Stephens M 

PROVIDER: S-EPMC5379932 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature