Composite Lognormal-T regression models with varying threshold and its insurance application

2022/08/02 by Girish Aradhye, Aradhye, Girish, Deepesh Bhati +3
Computer Science · Decision Sciences · Mathematics · #Applications (stat.AP) #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Probability and Risk Models #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2208.01262

openalex publication_date 2022/08/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Composite probability models have shown very promising results for modeling claim severity data comprised of small, moderate, and large losses. In this paper, we introduce three classes of parametric composite regression models with a varying threshold. We consider the Lognormal distribution for the head and the Burr, the Stoppa and the generalized log-Moyal (GlogM) distributions for the tail part of the composite family. Further, the Mode-Matching procedure has been utilized for the composition of the two densities. To capture the heterogeneous behavior of the policyholder's characteristics, covariates are introduced into the scale parameter of the tail distribution. Finally, the applicability of the proposed models has been shown using a real-world insurance data set.

Related

• A new class of composite GBII regression models with varying threshold for modelling heavy-tailed data
• Mixture composite regression models with multi-type feature selection
• Multivariate claim count regression model with varying dispersion and dependence parameters
• Generalizing the log-Moyal distribution and regression models for heavy tailed loss data
• A Copula-Based family of Bivariate Composite Models for Claim Severity Modelling