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Analyzing insurance data with an exponentiated composite inverse Gamma-Pareto model

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  • Bowen Liu
  • Malwane M. A. Ananda

Abstract

Exponentiated models have been widely used in modeling various types of data such as survival data and insurance claims data. However, the exponentiated composite distribution models have not been explored yet. In this paper, we introduce an improvement of the one-parameter Inverse Gamma-Pareto composite model by exponentiating the random variable associated with the one-parameter Inverse Gamma-Pareto composite distribution function. The goodness-of-fit of the exponentiated Inverse Gamma-Pareto was assessed using three different insurance data sets. The two-parameter exponentiated Inverse Gamma-Pareto model outperforms the one-parameter Inverse Gamma-Pareto model in terms of goodness-of-fit measures for all datasets. In addition, the proposed exponentiated composite Inverse Gamma-Pareto model provides a very good fit with some well-known insurance datasets.

Suggested Citation

  • Bowen Liu & Malwane M. A. Ananda, 2023. "Analyzing insurance data with an exponentiated composite inverse Gamma-Pareto model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(21), pages 7618-7631, November.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:21:p:7618-7631
    DOI: 10.1080/03610926.2022.2050399
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