Convex hierarchical testing of interactions

Abstract

We consider the testing of all pairwise interactions in a two-class problem with many features. We devise a hierarchical testing framework that considers an interaction only when one or more of its constituent features has a nonzero main effect. The test is based on a convex optimization framework that seamlessly considers main effects and interactions together. We show - both in simulation and on a genomic data set from the SAPPHIRe study - a potential gain in power and interpretability over a standard (nonhierarchical) interaction test.

Published at http://dx.doi.org/10.1214/14-AOAS758 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (4)

• In silico prediction of protein-protein interactions in human macrophages
• A lasso for hierarchical interactions
• A Permutation Approach to Testing Interactions in Many Dimensions
• Convex hierarchical testing of interactions

Cited by in corpus (2)

• Convex hierarchical testing of interactions
• Topological Techniques in Model Selection