Controlling the false discovery exceedance for heterogeneous tests

Abstract

Several classical methods exist for controlling the false discovery exceedance (FDX) for large-scale multiple testing problems, among them the Lehmann-Romano procedure (Lehmann and Romano 2005) ([LR] below) and the Guo-Romano procedure (Guo and Romano 2007) ([GR] below). While these two procedures are the most prominent, they were originally designed for homogeneous test statistics, that is, when the null distribution functions of the p-values Fi,1≤ i ≤ m, are all equal. In many applications, however, the data are heterogeneous which leads to heterogeneous null distribution functions. Ignoring this heterogeneity induces a lack of power. In this paper, we develop three new procedures that incorporate the Fi ’s, while maintaining rigorous FDX control. The heterogeneous version of [LR], denoted [HLR], is based on the arithmetic average of the Fi ’s, while the heterogeneous version of [GR], denoted [HGR], is based on the geometric average of the Fi ’s. We also introduce a procedure [PB], that is based on the Poisson-binomial distribution and that uniformly improves [HLR] and [HGR], at the price of a higher computational complexity. Per-haps surprisingly, this shows that, contrary to the known theory of false discovery rate (FDR) control under heterogeneity, the way to incorporate the Fi ’s can be particularly simple in the case of FDX control, and does not require any further correction term. The performances of the new proposed procedures are illustrated by real and simulated data in two important heterogeneous settings: first, when the test statistics are continuous but the p-values are weighted by some known independent weight vector, e.g., coming from co-data sets; second, when the test statistics are discretely dis-tributed, as is the case for data representing frequencies or counts. Our new procedures are implemented in the R package FDX, see Junge and Döhler (2020).