Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models
When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero...
| Authors: | ; ; |
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| Format: | Article |
| Status: | Published version |
| Publication date: | 2020 |
| Country: | España |
| Institution: | Universidad de Barcelona |
| Repository: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/149148 |
| Online Access: | https://hdl.handle.net/2445/149148 |
| Access Level: | Open access |
| Keyword: | Anàlisi de regressió Variables (Matemàtica) Assegurances d'automòbils Regression analysis Variables (Mathematics) Automobile insurance |
| Summary: | When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero-inflated, hurdle and compound frequency models are typically applied to insurance data to account for such a feature of the data. However, a natural way to deal with unobserved heterogeneity is to consider mixtures of a simpler models. In this paper, we consider k-finite mixtures of some typical regression models. (...) |
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