Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing

Abstract

Side information in addition to the p-values is often available in modern applications of multiple hypothesis testing. Here, the authors develop AdaFDR, a new statistical method for multiple hypothesis testing that adaptively learns the decision threshold and amplifies the discovery power.