Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258050 
Authors: 
Year of Publication: 
2020
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 8 [Issue:] 3 [Article No.:] 97 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-23
Publisher: 
MDPI, Basel
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.
Subjects: 
poisson-inverse gamma distribution
em algorithm
regression models for mean and dispersion parameters
motor third party liability insurance
ratemaking
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
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