A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection

2014/04/08 by Jeremy Sabourin, Sabourin, Jeremy, William Valdar +4
Computer Science · Engineering · Mathematics · #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Fault Detection and Control Systems #stat.ML

paper · pdf · doi:10.48550/arxiv.1404.2007

arxiv created 2014/04/08 · arxiv updated 2014/04/09

Abstract

We describe a simple, efficient, permutation based procedure for selecting the penalty parameter in the LASSO. The procedure, which is intended for applications where variable selection is the primary focus, can be applied in a variety of structural settings, including 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 three real data sets in which permutation selection is compared with cross-validation (CV), the Bayesian information criterion (BIC), and a selection method based on recently developed testing procedures for the LASSO.

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