A novel M-Lognormal–Burr regression model with varying threshold for modeling heavy-tailed claim severity data

Girish Aradhye, Deepesh Bhati, George Tzougas

Research output: Contribution to journalArticlepeer-review

Abstract

In this study, we explore the potential of composite probability distributions in effectively modeling claim severity data, which encompasses a spectrum of losses, ranging from minor to substantial. Our approach incorporates the innovative Mode-Matching technique to introduce a novel composite Lognormal–Burr distribution family. To comprehensively address the diverse risk characteristics exhibited by policyholders, we develop a regression model based on the composite Lognormal–Burr distribution. Additionally, we delve into the details of the parameter estimation method required for precise model parameter estimation. The practical utility of our proposed composite regression model is substantiated through its application to real-world insurance data, serving as a compelling illustration of its effectiveness.
Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalJournal of Applied Statistics
Early online date26 Feb 2024
DOIs
Publication statusE-pub ahead of print - 26 Feb 2024

Keywords

  • Burr distribution
  • composite regression model
  • generalized log-Moyal distribution
  • heterogeneity
  • mode-matching technique
  • varying threshold

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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