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Poisson regression and Zero-inflated Poisson regression: application to private health insurance data

Content Provider	Springer Nature Link
Author	Mouatassim, Younès Ezzahid, El Hadj
Copyright Year	2012
Abstract	Modeling event counts is important in many fields. For this purpose, the Poisson regression model is often used. However, this model assumes the equidispersion of the data. Unfortunately, this assumption is often violated in the observed data. The source of overdispersion depends on many situations. When the source of overdispersion is the excess of zeroes, the Zero-inflated Poisson regression model fits better counts data. In this paper, we first review the theoretical framework of Poisson regression and Zero-inflated Poisson regression. The probability integral transform test and the Vuong’s test are used to compare between the two models. Second, we fit these models to the number of claims in a private health insurance scheme. In our case, the number of claims is overdispersed because of the preponderance of zeroes in the data set. The results prove that Zero-inflated Poisson regression performs better the number of claims of the customers affiliated in the health insurance scheme in the Moroccan case.
Starting Page	187
Ending Page	204
Page Count	18
File Format	PDF
ISSN	21909733
Journal	European Actuarial Journal
Volume Number	2
Issue Number	2
e-ISSN	21909741
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-10-17
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Poisson model Zero-inflated Poisson model Excess of zeroes Vuong’s test Probability integral transform Goodness-of-fit Health insurance Finance/Investment/Banking Game Theory, Economics, Social and Behav. Sciences Applications of Mathematics Quantitative Finance
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Economics and Econometrics Statistics, Probability and Uncertainty

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