Selection of tuning parameters in bridge regression models via Bayesian information criterion

Abstract

We consider the bridge linear regression modeling, which can produce a sparse or non-sparse model. A crucial point in the model building process is the selection of adjusted parameters including a regularization parameter and a tuning parameter in bridge regression models. The choice of the adjusted parameters can be viewed as a model selection and evaluation problem. We propose a model selection criterion for evaluating bridge regression models in terms of Bayesian approach. This selection criterion enables us to select the adjusted parameters objectively. We investigate the effectiveness of our proposed modeling strategy through some numerical examples.

20 pages, 5 figures

References in corpus (4)

• Nearly unbiased variable selection under minimax concave penalty
• One-step sparse estimates in nonconcave penalized likelihood models
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models
• A unified approach to model selection and sparse recovery using regularized least squares

Cited by in corpus (1)

• The Minimum S-Divergence Estimator under Continuous Models: The Basu-Lindsay Approach