title: A permutation approach for selecting the penalty parameter in penalized model selection.
abstract: We describe a simple, computationally efficient, permutation-based procedure for selecting the penalty parameter in LASSO-penalized regression. The procedure, permutation selection, is intended for applications where variable selection is the primary focus, and can be applied in a variety of structural settings, including that of generalized linear models. We briefly discuss connections between permutation selection and existing theory for the LASSO. In addition, we present a simulation study and an analysis of real biomedical data sets in which permutation selection is compared with selection based on the following: cross-validation (CV), the Bayesian information criterion (BIC), scaled sparse linear regression, and a selection method based on recently developed testing procedures for the LASSO.
authors.affiliation: Department of Genetics, University of North Carolina at Chapel Hill, North Carolina, U.S.A.|Genometrics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, U.S.A.
authors.forename: Jeremy A
authors.initials: JA
authors.lastname: Sabourin
authors.affiliation: Department of Genetics, University of North Carolina at Chapel Hill, North Carolina, U.S.A.|Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, North Carolina, U.S.A.
authors.forename: William
authors.initials: W
authors.lastname: Valdar
authors.affiliation: Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, North Carolina, U.S.A.|Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, North Carolina, U.S.A.|Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, U.S.A.
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Nobel
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D001943:Breast Neoplasms / Q000235:genetics; D008076:Cholesterol, HDL / Q000097:blood / Q000235:genetics; D003198:Computer Simulation; D016208:Databases, Factual / Q000706:statistics & numerical data; D005260:Female; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D051379:Mice; D015233:Models, Statistical; D012044:Regression Analysis
chemical_list: D008076:Cholesterol, HDL
keywords: LASSO; Penalized regression; Variable selection