The knockoff filter for FDR control in group-sparse and multitask regression

TL;DR

This paper introduces the group knockoff filter, a new method for controlling false discovery rate in group-sparse and multitask regression, improving power by leveraging group structures.

Contribution

The paper proposes the group knockoff filter, extending FDR control to grouped features and multitask settings, with demonstrated empirical success.

Findings

Successfully controls false discoveries at the group level

Achieves more discoveries by leveraging group structure

Effective in both group-sparse and multitask regression

Abstract

We propose the group knockoff filter, a method for false discovery rate control in a linear regression setting where the features are grouped, and we would like to select a set of relevant groups which have a nonzero effect on the response. By considering the set of true and false discoveries at the group level, this method gains power relative to sparse regression methods. We also apply our method to the multitask regression problem where multiple response variables share similar sparsity patterns across the set of possible features. Empirically, the group knockoff filter successfully controls false discoveries at the group level in both settings, with substantially more discoveries made by leveraging the group structure.