Bermúdez, LluísKarlis, DimitrisMorillo, Isabel2020-01-312020-01-312020-012227-9091https://hdl.handle.net/2445/149148When 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. (...)13 p.application/pdfengcc-by (c) Bermúdez, Lluís et al., 2020http://creativecommons.org/licenses/by/3.0/esAnàlisi de regressióVariables (Matemàtica)Assegurances d'automòbilsRegression analysisVariables (Mathematics)Automobile insuranceinfo:eu-repo/semantics/article6953322020-01-31info:eu-repo/semantics/openAccess