Bayesian space-time analysis of health insurance data

Generalized linear models (GLMs) and semiparametric extensions
provide a flexible framework for analyzing the claims process in
non-life insurance. Currently, most applications are still based on
traditional GLMs, where covariate effects are modelled in form of a
linear predictor. However, these models may already be too
restrictive if nonlinear effects of metrical covariates are
present. Moreover, although data are often collected within longer
time periods and come from different geographical regions, effects
of space and time are usually totally neglected. We provide a
Bayesian semiparametric approach, which allows to simultaneously
incorporate effects of space, time and further covariates within a
joint model. The method is applied to analyze costs of hospital
treatment and accommodation for a large data set from a German
health insurance company.