Access Restriction

Author Fahrmeir, Ludwig ♦ Osuna, Leyre
Source EconStor
Content type Text
Publisher Techn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen cMünchen
File Format PDF ♦ PS / EPS
Language English
Subject Domain (in DDC) Social sciences ♦ Collections of general statistics
Subject Keyword Bayesian semiparametric count data regression ♦ negative binomial distribution ♦ Poisson-Gamma distribution ♦ Poisson-Log-Normal distribution ♦ MCMC ♦ spatial models
Abstract Overdispersion in count data regression is often caused by neglection or inappropriate modelling of individual heterogeneity, temporal or spatial correlation, and nonlinear covariate effects. In this paper, we develop and study semiparametric count data models which can deal with these issues by incorporating corresponding components in structured additive form into the predictor. The models are fully Bayesian and inference is carried out by computationally efficient MCMC techniques. In a simulation study, we investigate how well the different components can be identified with the data at hand. The approach is applied to a large data set of claim frequencies from car insurance.
Part of series Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München x334
Learning Resource Type Article
Publisher Date 2003-01-01
Rights Holder