Controlling the false discovery rate under non-parametric graphical dependencies

Music City Center 

We propose sufficient conditions and procedures for false discovery rate (FDR) control in multiple testing when the p-values are related by a known dependency graph---meaning that we assume mutual independence of p-values not within each other's neighborhoods. Often this dependence is known to be local, implying a sparse graph, but in general the dependence can be partially known. Our main FDR controlling procedure reduces to the Bonferroni correction for fully connected graphs and the usual Benjamini-Hochberg (BH) procedure under independence or PRDS.
Though our main method can be computationally intensive relative to BH, it runs with reasonable wall-clock time even with m = 10^6 hypotheses. Simulations and real data examples establish that its power is typically almost identical to BH. It also typically dominates an alternate approach which reduces to the Benjamini-Yekutieli (BY) correction on fully connected graphs.

false discovery rate

Benjamini-Hochberg

dependence

non-parametric

graphs 

IMS