Em estimation for the poisson-inverse gamma regression model with varying dispersion: An application to insurance ratemaking

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3 Citations (Scopus)

Abstract

This article presents the Poisson-Inverse Gamma regression model with varying dispersion for approximating heavy-tailed and overdispersed claim counts. Our main contribution is that we develop an Expectation-Maximization (EM) type algorithm for maximum likelihood (ML) estimation of the Poisson-Inverse Gamma regression model with varying dispersion. The empirical analysis examines a portfolio of motor insurance data in order to investigate the efficiency of the proposed algorithm. Finally, both the a priori and a posteriori, or Bonus-Malus, premium rates that are determined by the Poisson-Inverse Gamma model are compared to those that result from the classic Negative Binomial Type I and the Poisson-Inverse Gaussian distributions with regression structures for their mean and dispersion parameters.

Original languageEnglish
Article number97
JournalRisks
Volume8
Issue number3
DOIs
Publication statusPublished - 11 Sep 2020

Keywords

  • Em algorithm
  • Motor third party liability insurance
  • Poisson-inverse gamma distribution
  • Ratemaking
  • Regression models for mean and dispersion parameters

ASJC Scopus subject areas

  • Accounting
  • Economics, Econometrics and Finance (miscellaneous)
  • Strategy and Management

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