Weighted logistic regression to improve predictive performance in insurance

Abstract

We propose a logistic regression model combined with a weighting estimation procedure that incorporates a tuning parameter. We analyse predictive performance indicators. Results show that the parameter defining the weights can be used to improve predictive accuracy, at least when the original predictive value is distant from the response average. We use a publicly available data set to illustrate our method and we discuss the potential benefits of this methodology in the decision to purchase full coverage motor insurance versus a basic insurance product.