The multivariate Poisson-Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters.

Item Type Journal paper
Abstract We introduce a multivariate Poisson-Generalized Inverse Gaussian regression model with varying dispersion and shape for modeling different types of claims and their associated counts in nonlife insurance. The multivariate Poisson-Generalized Inverse Gaussian regression model is a general class of models which, under the approach adopted herein, allows us to account for overdispersion and positive correlation between the claim count responses in a flexible manner. For expository purposes, we consider the bivariate Poisson-Generalized Inverse Gaussian with regression structures on the mean, dispersion, and shape parameters. The model's implementation is demonstrated by using bodily injury and property damage claim count data from a European motor insurer. The parameters of the model are estimated via the Expectation-Maximization algorithm which is computationally tractable and is shown to have a satisfactory performance.
Authors Tzougas, George & Makariou, Despoina
Journal or Publication Title Risk Management and Insurance Review
Language English
Subjects computer science
social sciences
other research area
finance
statistics
HSG Classification contribution to scientific community
Refereed Yes
Date 17 October 2022
Publisher Wiley-Blackwell
ISSN 1098-1616
Publisher DOI https://doi.org/10.1111/rmir.12224
Official URL https://onlinelibrary.wiley.com/doi/full/10.1111/r...
Depositing User Prof. Dr. Despoina Makariou
Date Deposited 02 Dec 2022 11:22
Last Modified 02 Dec 2022 11:22
URI: https://www.alexandria.unisg.ch/publications/268234

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Tzougas, George & Makariou, Despoina (2022) The multivariate Poisson-Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters. Risk Management and Insurance Review, ISSN 1098-1616

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https://www.alexandria.unisg.ch/id/eprint/268234
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