Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/149148
Title: | Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models |
Author: | Bermúdez, Lluís Karlis, Dimitris Morillo, Isabel |
Keywords: | Anàlisi de regressió Variables (Matemàtica) Assegurances d'automòbils Regression analysis Variables (Mathematics) Automobile insurance |
Issue Date: | Jan-2020 |
Publisher: | MDPI |
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. (...) |
Note: | Reproducció del document publicat a: https://doi.org/10.3390/risks8010010 |
It is part of: | Risks , 2020, vol. 8, num. 1(10), p. 01-13 |
URI: | http://hdl.handle.net/2445/149148 |
Related resource: | https://doi.org/10.3390/risks8010010 |
ISSN: | 2227-9091 |
Appears in Collections: | Articles publicats en revistes (Matemàtica Econòmica, Financera i Actuarial) |
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