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- W4306697788 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." @default.
- W4306697788 created "2022-10-19" @default.
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- W4306697788 date "2022-10-17" @default.
- W4306697788 modified "2023-09-27" @default.
- W4306697788 title "The multivariate Poisson‐Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters" @default.
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- W4306697788 doi "https://doi.org/10.1111/rmir.12224" @default.
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