Consistent Group Identification and Variable Selection in Regression with Correlated Predictors.

TitleConsistent Group Identification and Variable Selection in Regression with Correlated Predictors.
Publication TypeJournal Article
Year of Publication2013
AuthorsSharma, Dhruv B., Howard D. Bondell, and Hao Helen Zhang
JournalJ Comput Graph Stat
Volume22
Issue2
Pagination319-340
Date Published2013 Apr 01
ISSN1061-8600
Abstract

Statistical procedures for variable selection have become integral elements in any analysis. Successful procedures are characterized by high predictive accuracy, yielding interpretable models while retaining computational efficiency. Penalized methods that perform coefficient shrinkage have been shown to be successful in many cases. Models with correlated predictors are particularly challenging to tackle. We propose a penalization procedure that performs variable selection while clustering groups of predictors automatically. The oracle properties of this procedure including consistency in group identification are also studied. The proposed method compares favorably with existing selection approaches in both prediction accuracy and model discovery, while retaining its computational efficiency. Supplemental material are available online.

DOI10.1080/15533174.2012.707849
Alternate JournalJ Comput Graph Stat
Original PublicationConsistent group identification and variable selection in regression with correlated predictors.
PubMed ID23772171
PubMed Central IDPMC3678393
Grant ListP01 CA134294 / CA / NCI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 CA085848 / CA / NCI NIH HHS / United States
R01 MH084022 / MH / NIMH NIH HHS / United States