Mixture composite regression models with multi-type feature selection

TL;DR

This paper introduces a novel mixture composite regression model for claim severity analysis, effectively handling multimodality, heavy tails, and systematic effects, with a new regularization and estimation approach demonstrated on insurance data.

Contribution

It proposes a new mixture composite regression model with a group-fused regularization method and a generalized EM algorithm for claim severity modeling, addressing complex data characteristics.

Findings

Effective variable selection for mixture components and probabilities.

Model captures multimodality and heavy tails in claim data.

Demonstrated on real motor insurance dataset.

Abstract

The aim of this paper is to present a mixture composite regression model for claim severity modelling. Claim severity modelling poses several challenges such as multimodality, heavy-tailedness and systematic effects in data. We tackle this modelling problem by studying a mixture composite regression model for simultaneous modeling of attritional and large claims, and for considering systematic effects in both the mixture components as well as the mixing probabilities. For model fitting, we present a group-fused regularization approach that allows us for selecting the explanatory variables which significantly impact the mixing probabilities and the different mixture components, respectively. We develop an asymptotic theory for this regularized estimation approach, and fitting is performed using a novel Generalized Expectation-Maximization algorithm. We exemplify our approach on real motor insurance data set.