In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. We account for unobserved heterogeneity in the data in two ways. On the one hand, we consider more flexible models than a common Poisson model allowing for overdispersion in different...

In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. Besides the inclusion of covariates, spatial effects are incorporated and modelled using a proper Gaussian conditional autoregressive prior based on Pettitt et al. (2002). Apart from...

This paper focuses on an extension of zero-inflated generalized Poisson (ZIGP) regression models for count data. We discuss generalized Poisson (GP) models where dispersion is modelled by an additional model parameter. Moreover, zero-inflated models in which overdispersion is assumed to be...

We present a joint copula-based model for insurance claims and sizes. It uses bivariate copulae to accommodate for the dependence between these quantities. We derive the general distribution of the policy loss without the restrictive assumption of independence. We illustrate that this...