Adaptive bridge regression modeling with model selection criteria

TL;DR

This paper introduces an adaptive bridge regression method that uses model selection criteria derived from information-theoretic and Bayesian approaches to optimize penalized modeling with weighted coefficients.

Contribution

It proposes a novel adaptive bridge regression framework with new model selection criteria for choosing adjusted parameters effectively.

Findings

Numerical studies demonstrate the effectiveness of the proposed method.

The approach improves model selection accuracy.

The method outperforms existing techniques in simulations.

Abstract

We consider the problem of constructing an adaptive bridge regression modeling, which is a penalized procedure by imposing different weights to different coefficients in the bridge penalty term. A crucial issue in the modeling process is the choices of adjusted parameters included in the models. We treat the selection of the adjusted parameters as model selection and evaluation problems. In order to select the parameters, model selection criteria are derived from information-theoretic and Bayesian approach. We conduct some numerical studies to investigate the effectiveness of our proposed modeling strategy.