A permutation approach for selecting the penalty parameter in penalized model selection
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Updated: Apr 6, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Jeremy A Sabourin1,2, William Valdar1,3, Andrew B Nobel4,3,5
1Department of Genetics, University of North Carolina at Chapel Hill, North Carolina, U.S.A.
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.
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