Convex hierarchical testing of interactions
Methodology
2015-06-03 v2
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.
Keywords
Cite
@article{arxiv.1211.1344,
title = {Convex hierarchical testing of interactions},
author = {Jacob Bien and Noah Simon and Robert Tibshirani},
journal= {arXiv preprint arXiv:1211.1344},
year = {2015}
}
Comments
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)