A permutation approach for selecting the penalty parameter in penalized model selection

Biometrics. 2015 Dec;71(4):1185-94. doi: 10.1111/biom.12359. Epub 2015 Aug 3.

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.

Keywords: LASSO; Penalized regression; Variable selection.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, N.I.H., Intramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Bayes Theorem
  • Biometry / methods
  • Breast Neoplasms / genetics
  • Cholesterol, HDL / blood
  • Cholesterol, HDL / genetics
  • Computer Simulation
  • Databases, Factual / statistics & numerical data
  • Female
  • Genome-Wide Association Study / statistics & numerical data
  • Humans
  • Linear Models
  • Logistic Models
  • Mice
  • Models, Statistical*
  • Regression Analysis

Substances

  • Cholesterol, HDL