Showing 1 - 10 of 17
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...
Persistent link: https://www.econbiz.de/10010266214
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...
Persistent link: https://www.econbiz.de/10002753399
In this paper we present and evaluate a Gibbs sampler for a Poisson regression model including spatial effects. The approach is based on Frühwirth-Schnatter and Wagner (2004b) who show that by data augmentation using the introduction of two sequences of latent variables a Poisson regression...
Persistent link: https://www.econbiz.de/10003309987
In this paper models for claim frequency and claim size in non-life insurance are considered. Both covariates and spatial random effects are included allowing the modelling of a spatial dependency pattern. We assume a Poisson model for the number of claims, while claim size is modelled using a...
Persistent link: https://www.econbiz.de/10003310005
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...
Persistent link: https://www.econbiz.de/10003310097
Persistent link: https://www.econbiz.de/10003715380
Persistent link: https://www.econbiz.de/10008925119
We propose a new class of state space models for longitudinal discrete response data where the observation equation is specified in an additive form involving both deterministic and random linear predictors. These models allow us to explicitly address the effects of trend, seasonal or other...
Persistent link: https://www.econbiz.de/10010266157
In this paper we investigate intraday data of the IBM stock and a time series representing the sleep states of a newborn child. In both cases we are interested in the influence of several covariates observed together with the response series. For the purpose we use on the one hand the regression...
Persistent link: https://www.econbiz.de/10010275911
Persistent link: https://www.econbiz.de/10008533757