Structured, sparse regression with application to HIV drug resistance
Abstract
We introduce a new version of forward stepwise regression. Our modification finds solutions to regression problems where the selected predictors appear in a structured pattern, with respect to a predefined distance measure over the candidate predictors. Our method is motivated by the problem of predicting HIV-1 drug resistance from protein sequences. We find that our method improves the interpretability of drug resistance while producing comparable predictive accuracy to standard methods. We also demonstrate our method in a simulation study and present some theoretical results and connections.
Cite
@article{arxiv.1002.3128,
title = {Structured, sparse regression with application to HIV drug resistance},
author = {Daniel Percival and Kathryn Roeder and Roni Rosenfeld and Larry Wasserman},
journal= {arXiv preprint arXiv:1002.3128},
year = {2011}
}
Comments
Published in at http://dx.doi.org/10.1214/10-AOAS428 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)