A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection
Machine Learning
2014-04-09 v1
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.
Keywords
Cite
@article{arxiv.1404.2007,
title = {A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection},
author = {Jeremy Sabourin and William Valdar and Andrew Nobel},
journal= {arXiv preprint arXiv:1404.2007},
year = {2014}
}