Document Detail


A Group Bridge Approach for Variable Selection.
MedLine Citation:
PMID:  20037673     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
In multiple regression problems when covariates can be naturally grouped, it is important to carry out feature selection at the group and within-group individual-variable levels simultaneously. The existing methods, including the Lasso and group Lasso, are designed for either variable selection or group selection, but not for both. We propose a group bridge approach that it is capable of simultaneous selection at both the group and within-group individual variable levels. The proposed approach is a penalized regularization method that uses a specially designed group bridge penalty. It has the oracle group selection property, in that it can correctly select important groups with probability converging to one. In contrast, the group Lasso and group least angle regression methods in general do not possess such an oracle property in group selection. Simulation studies indicate that the group bridge has superior performance in group and individual variable selection relative to several existing methods.
Authors:
Jian Huang; Shuangge Ma; Huiliang Xie; Cun-Hui Zhang
Related Documents :
7016123 - Trial designs for multicentre clinical studies of investigational phases i b to iii wit...
8792953 - The role of dornase alfa in the treatment of cystic fibrosis.
19250263 - Radioactive iodine for hyperthyroidism in children and adolescents: referral rate and r...
Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Biometrika     Volume:  96     ISSN:  0006-3444     ISO Abbreviation:  -     Publication Date:  2009 Jun 
Date Detail:
Created Date:  2009-12-28     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0413661     Medline TA:  Biometrika     Country:  -    
Other Details:
Languages:  ENG     Pagination:  339-355     Citation Subset:  -    
Affiliation:
Department of Statistics and Actuarial Science, University of Iowa, 221 Schaeffer Hall, Iowa City, Iowa 52242, U.S.A., jian-huang@uiowa.edu.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Grant Support
ID/Acronym/Agency:
R01 CA120988-01A2//NCI NIH HHS; R01 CA120988-02//NCI NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Electronic Diaries: Appraisal and Current Status.
Next Document:  Diagnostic Measures for Generalized Linear Models with Missing Covariates.