A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection
This work addresses a specific challenge in penalized model selection for statisticians and data scientists, but it is incremental as it builds on existing LASSO theory and methods.
The authors tackled the problem of selecting the penalty parameter in LASSO for variable selection by proposing a permutation-based method, which was compared to cross-validation, BIC, and testing-based methods in simulations and real data analyses, showing competitive performance.
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.