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EM estimation for the mixed Pareto regression model for claim severities

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  • Girish Aradhye
  • George Tzougas
  • Deepesh Bhati

Abstract

This article presents a mixed Pareto model and examines its suitability for modeling non life insurance claim severity data sets which exhibit peculiar characteristics that cannot be captured by the Pareto distribution. Furthermore, we introduce regression specifications for both the mean and the dispersion parameters of the mixed Pareto model. Our main achievement is that we develop a novel Expectation-Maximization (EM) algorithm for finding the maximum likelihood (ML) estimates of the parameters of the mixed Pareto regression model which is used for demonstration purposes. Finally, a real-data application based on motor insurance claim size data is provided.

Suggested Citation

  • Girish Aradhye & George Tzougas & Deepesh Bhati, 2024. "EM estimation for the mixed Pareto regression model for claim severities," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 53(15), pages 5507-5523, August.
    • Handle: RePEc:taf:lstaxx:v:53:y:2024:i:15:p:5507-5523
    DOI: 10.1080/03610926.2023.2221358
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