Abstract
It is of interest for an insurance company to model the claim count or the claim severity in the presence of covariates or covariate factors describing the policyholders. In this paper the Poisson regression is presented (in the context of generalized linear models) and fitted to car insurance claims data. Since there are symptoms of over-dispersion in the data, the Poisson distribution of the response variable is replaced with the negative binomial distribution and another model is fitted to the number of claims. Finally, a method of testing the significance of the differences between groups of policyholders is shown and applied to the negative binomial regression.
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Bartoszewicz, B. (2005). Modelling the Claim Count with Poisson Regression and Negative Binomial Regression. In: Baier, D., Wernecke, KD. (eds) Innovations in Classification, Data Science, and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26981-9_13
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DOI: https://doi.org/10.1007/3-540-26981-9_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23221-6
Online ISBN: 978-3-540-26981-6
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