Mixture Composite Regression Models with Multi-type Feature Selection

Tsz Chai Fung*, George Tzougas, Mario V. Wüthrich

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The aim of this article is to present a mixture composite regression model for claim severity modeling. Claim severity modeling poses several challenges such as multimodality, tail-heaviness, and systematic effects in data. We tackle this modeling 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 to select the explanatory variables that 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 a real motor insurance dataset.

Original languageEnglish
JournalNorth American Actuarial Journal
Early online date22 Aug 2022
DOIs
Publication statusE-pub ahead of print - 22 Aug 2022

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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