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

Girish Aradhye, Deepesh Bhati and George Tzougas

Journal of Applied Statistics, 2024, vol. 51, issue 14, 2832-2850

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.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:51:y:2024:i:14:p:2832-2850

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DOI: 10.1080/02664763.2024.2319232

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