EM estimation for the mixed Pareto regression model for claim severities

Girish Aradhye, George Tzougas, Deepesh Bhati

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalCommunications in Statistics - Theory and Methods
Early online date13 Jun 2023
Publication statusE-pub ahead of print - 13 Jun 2023


  • Expectation-Maximization algorithm
  • Inverse Gaussian distribution
  • Motor insurance claim severity data
  • mixed model
  • regression models

ASJC Scopus subject areas

  • Statistics and Probability


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