This article demonstrates how the bayesian variable selection method enhances feature importance in linear regression models for telematics data, indicating practical benefits.
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.
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Jeong et al. (2025) studied this question.
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