Selection of tuning parameters in bridge regression models via Bayesian information criterion
This work addresses parameter selection in bridge regression for statistical modeling, but it is incremental as it builds on existing methods.
The authors tackled the problem of selecting regularization and tuning parameters in bridge regression models by proposing a Bayesian information criterion for objective parameter selection, and demonstrated its effectiveness through numerical examples.
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.