Varying-coefficient modeling via regularized basis functions

TL;DR

This paper introduces a method for constructing varying-coefficient models using basis expansions and regularization, with an objective approach for selecting smoothing parameters based on information-theoretic and Bayesian criteria, validated through simulations and real data analysis.

Contribution

It proposes a novel model selection approach for regularized basis function-based varying-coefficient models, improving parameter choice objectivity.

Findings

Effective smoothing parameter selection criteria derived from information theory and Bayesian methods.

Demonstrated improved model performance through simulations.

Validated approach with real data analysis.

Abstract

We address the problem of constructing varying-coefficient models based on basis expansions along with the technique of regularization. A crucial point in our modeling procedure is the selection of smoothing parameters in the regularization method. In order to choose the parameters objectively, we derive model selection criteria from the viewpoints of information-theoretic and Bayesian approach. We demonstrate the effectiveness of proposed modeling strategy through Monte Carlo simulations and analyzing a real data set.