Document Detail


Weighted Wilcoxon-type smoothly clipped absolute deviation method.
MedLine Citation:
PMID:  18647294     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Shrinkage-type variable selection procedures have recently seen increasing applications in biomedical research. However, their performance can be adversely influenced by outliers in either the response or the covariate space. This article proposes a weighted Wilcoxon-type smoothly clipped absolute deviation (WW-SCAD) method, which deals with robust variable selection and robust estimation simultaneously. The new procedure can be conveniently implemented with the statistical software R. We establish that the WW-SCAD correctly identifies the set of zero coefficients with probability approaching one and estimates the nonzero coefficients with the rate n(-1/2). Moreover, with appropriately chosen weights the WW-SCAD is robust with respect to outliers in both the x and y directions. The important special case with constant weights yields an oracle-type estimator with high efficiency in the presence of heavier-tailed random errors. The robustness of the WW-SCAD is partly justified by its asymptotic performance under local shrinking contamination. We propose a Bayesian information criterion type tuning parameter selector for the WW-SCAD. The performance of the WW-SCAD is demonstrated via simulations and by an application to a study that investigates the effects of personal characteristics and dietary factors on plasma beta-carotene level.
Authors:
Lan Wang; Runze Li
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2008-07-18
Journal Detail:
Title:  Biometrics     Volume:  65     ISSN:  1541-0420     ISO Abbreviation:  Biometrics     Publication Date:  2009 Jun 
Date Detail:
Created Date:  2009-06-23     Completed Date:  2009-09-02     Revised Date:  2013-06-05    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  564-71     Citation Subset:  IM    
Affiliation:
School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455, USA. lan@stat.umn.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Biometry / methods*
Computer Simulation
Data Interpretation, Statistical*
Epidemiologic Research Design*
Models, Statistical*
Reproducibility of Results
Sensitivity and Specificity
Statistics, Nonparametric*
Grant Support
ID/Acronym/Agency:
1 R21 DA024260/DA/NIDA NIH HHS; R21 DA024260-01/DA/NIDA NIH HHS; R21 DA024260-02/DA/NIDA NIH HHS
Comments/Corrections

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