Sufficient Dimension Reduction for Interactions
Methodology
2020-06-02 v1
Abstract
Dimension reduction lies at the heart of many statistical methods. In regression, dimension reduction has been linked to the notion of sufficiency whereby the relation of the response to a set of predictors is explained by a lower dimensional subspace in the predictor space. In this paper, we consider the notion of a dimension reduction in regression on subspaces that are sufficient to explain interaction effects between predictors and another variable of interest. The motivation for this work is from precision medicine where the performance of an individualized treatment rule, given a set of pretreatment predictors, is determined by interaction effects.
Keywords
Cite
@article{arxiv.2006.00261,
title = {Sufficient Dimension Reduction for Interactions},
author = {Hyung Park and Eva Petkova and Thaddeus Tarpey and R. Todd Ogden},
journal= {arXiv preprint arXiv:2006.00261},
year = {2020}
}
Comments
25 pages, 3 figures