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A permutation approach for selecting the penalty parameter in penalized model selection
Author(s) -
Sabourin Jeremy A.,
Valdar William,
Nobel Andrew B.
Publication year - 2015
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12359
Subject(s) - lasso (programming language) , permutation (music) , selection (genetic algorithm) , model selection , computer science , feature selection , focus (optics) , bayesian information criterion , generalized linear model , bayesian probability , linear model , linear regression , resampling , algorithm , mathematical optimization , machine learning , mathematics , artificial intelligence , physics , world wide web , acoustics , optics
Summary 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.