Co-sufficient weighting: leveraging sparsity for more powerful variable selection in the linear model

Abstract

We show that for the problem of controlled variable selection in the Gaussian linear model, informative and p-value-independent weights (for weighted multiple testing) can be derived entirely from sufficient statistics and a belief in sparsity using only the data itself and no external quantitative side information. Using these weights, which we call \emph{co-sufficient weights} (CSW), in weighted versions of Holm's procedure and the Benjamini--Hochberg (BH) procedure results in substantially higher power than their unweighted analogues when the true coefficient vector is sparse, while retaining Holm's exact validity and BH's highly robust empirical validity regardless of sparsity. We demonstrate the superior performance of our proposed weighted methods compared to their unweighted analogues and other competing methods in a wide range of simulations and an application to HIV drug resistance.