Bayesian Space-time Analysis of Health Insurance Data View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2003

AUTHORS

S. Lang , P. Kragler , G. Haybach , L. Fahrmeir

ABSTRACT

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. More... »

PAGES

133-140

References to SciGraph publications

  • 2001-03. Bayesian Semiparametric Regression Analysis of Multicategorical Time-Space Data in ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
  • Book

    TITLE

    Exploratory Data Analysis in Empirical Research

    ISBN

    978-3-540-44183-0
    978-3-642-55721-7

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-55721-7_15

    DOI

    http://dx.doi.org/10.1007/978-3-642-55721-7_15

    DIMENSIONS

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