Bayesian Approach to Simultaneous Variable Selection and Estimation in a Linear Regression Model with Applications in Driver Telematics

This article proposes a novel application of the Bayesian variable selection framework for driver telematics data. Unlike the traditional LASSO, the Bayesian variable selection framework allows us to incorporate the importance of certain features in advance in the variable selection procedure so that the traditional features more likely remain in the ratemaking models. The applicability of the proposed framework in the ratemaking practices is also validated via synthetic telematics data.