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Open AccessArticle

EM Estimation for the Poisson-Inverse Gamma Regression Model with Varying Dispersion: An Application to Insurance Ratemaking

Department of Statistics, London School of Economics and Political Science, London WC2A 2AE, UK
Risks 2020, 8(3), 97; https://doi.org/10.3390/risks8030097
Received: 19 August 2020 / Revised: 6 September 2020 / Accepted: 8 September 2020 / Published: 11 September 2020
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. View Full-Text
Keywords: poisson-inverse gamma distribution; em algorithm; regression models for mean and dispersion parameters; motor third party liability insurance; ratemaking poisson-inverse gamma distribution; em algorithm; regression models for mean and dispersion parameters; motor third party liability insurance; ratemaking
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MDPI and ACS Style

Tzougas, G. EM Estimation for the Poisson-Inverse Gamma Regression Model with Varying Dispersion: An Application to Insurance Ratemaking. Risks 2020, 8, 97. https://doi.org/10.3390/risks8030097

AMA Style

Tzougas G. EM Estimation for the Poisson-Inverse Gamma Regression Model with Varying Dispersion: An Application to Insurance Ratemaking. Risks. 2020; 8(3):97. https://doi.org/10.3390/risks8030097

Chicago/Turabian Style

Tzougas, George. 2020. "EM Estimation for the Poisson-Inverse Gamma Regression Model with Varying Dispersion: An Application to Insurance Ratemaking" Risks 8, no. 3: 97. https://doi.org/10.3390/risks8030097

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