Files

695332.pdf (363.94 KB)

Document type

Article

Version

Published version

Publication date

2020-01

Publication license

cc-by (c) Bermúdez, Lluís et al., 2020
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/149148

Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models

Journal Title

Authors

Director/Tutor

Journal ISSN

Volume Title

Related resource

https://doi.org/10.3390/risks8010010

Abstract

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. (...)

Subject

Anàlisi de regressió, Variables (Matemàtica), Assegurances d'automòbils

Subject (English)

Regression analysis, Variables (Mathematics), Automobile insurance

Citation

Collections

Articles publicats en revistes (Matemàtica Econòmica, Financera i Actuarial)

Citation

BERMÚDEZ, Lluís, KARLIS, Dimitris and MORILLO, Isabel. Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models. Risks . 2020. Vol. 8, num. 1(10), pags. 01-13. ISSN 2227-9091. [consulted: 9 of June of 2026]. Available at: https://hdl.handle.net/2445/149148

Export metadata

JSON - METS

Share record