Adaptive False Discovery Rate Control under Independence and Dependence
Gilles Blanchard and Etienne Roquain
Journal of Machine Learning Research
In the context of multiple hypothesis testing, the proportion π0 of true null hypotheses in the pool of hypotheses to test often plays a crucial role, although it is generally unknown a priori. A testing procedure using an implicit or explicit estimate of this quantity in order to improve its efficency is called adaptive. In this paper, we focus on the issue of false discovery rate (FDR) control and we present new adaptive multiple testing procedures with control of the FDR. In a first part, assuming independence of the p-values, we present two new procedures and give a unified review of other existing adaptive procedures that have provably controlled FDR. We report extensive simulation results comparing these procedures and testing their robustness when the independence assumption is violated. The new proposed procedures appear competitive with existing ones. The overall best, though, is reported to be Storey's estimator, albeit for a specific parameter setting that does not appear to have been considered before. In a second part, we propose adaptive versions of step-up procedures that have provably controlled FDR under positive dependence and unspecified dependence of the p-values, respectively. In the latter case, while simulations only show an improvement over non-adaptive procedures in limited situations, these are to our knowledge among the first theoretically founded adaptive multiple testing procedures that control the FDR when the p-values are not independent.