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- Title
The Applications of Generalized Poisson Regression Models to Insurance Claim Data.
- Authors
Faroughi, Pouya; Li, Shu; Ren, Jiandong
- Abstract
Predictive modeling has been widely used for insurance rate making. In this paper, we focus on insurance claim count data and address their common issues with more flexible modeling techniques. In particular, we study the zero-inflated and hurdle-generalized Poisson and negative binomial distributions in a functional form for modeling insurance claim count data. It is shown that these models are useful in addressing the problem of excess zeros and over-dispersion of the claim count variable. In addition, we show that including the exposure as a covariate in both the zero and the count part of the model is an effective approach to incorporating exposure information in zero-inflated and hurdle models. We illustrate the effectiveness and versatility of the introduced models using three real datasets. The results suggest their promising applications in insurance risk classification and beyond.
- Subjects
POISSON regression; INSURANCE claims; NEGATIVE binomial distribution; REGRESSION analysis; INSURANCE rates
- Publication
Risks, 2023, Vol 11, Issue 12, p213
- ISSN
2227-9091
- Publication type
Article
- DOI
10.3390/risks11120213