EM estimation for the Poisson-Inverse Gamma regression model with varying dispersion: an application to insurance ratemaking

Tzougas, George (2020) EM estimation for the Poisson-Inverse Gamma regression model with varying dispersion: an application to insurance ratemaking. Risks, 8 (3). pp. 1-23. ISSN 2227-9091

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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.

Item Type:	Article
Official URL:	https://www.mdpi.com/journal/risks
Additional Information:	© 2020 The Author
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	11 Sep 2020 11:30
Last Modified:	01 Nov 2024 23:45
URI:	http://eprints.lse.ac.uk/id/eprint/106539

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