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Lang, S.; Kragler, P.; Haybach, G. and Fahrmeir, Ludwig (2001): Bayesian space-time analysis of health insurance data. Collaborative Research Center 386, Discussion Paper 237 [PDF, 598kB]


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.

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