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Applications of Multilevel Structured Additive Regression Models to Insurance Data

In: Proceedings of COMPSTAT'2010

Author

Listed:
  • Stefan Lang

    (University of Innsbruck, Department of Statistics)

  • Nikolaus Umlauf

    (University of Innsbruck, Department of Statistics)

Abstract

Models with structured additive predictor provide a very broad and rich framework for complex regression modeling. They can deal simultaneously with nonlinear covariate effects and time trends, unit- or cluster specific heterogeneity, spatial heterogeneity and complex interactions between covariates of different type. In this paper, we discuss a hierarchical version of regression models with structured additive predictor and its applications to insurance data. That is, the regression coefficients of a particular nonlinear term may obey another regression model with structured additive predictor. The proposed model may be regarded as a an extended version of a multilevel model with nonlinear covariate terms in every level of the hierarchy. We describe several highly efficient MCMC sampling schemes that allow to estimate complex models with several hierarchy levels and a large number of observations typically within a couple of minutes. We demonstrate the usefulness of the approach with applications to insurance data.

Suggested Citation

  • Stefan Lang & Nikolaus Umlauf, 2010. "Applications of Multilevel Structured Additive Regression Models to Insurance Data," Springer Books, in: Yves Lechevallier & Gilbert Saporta (ed.), Proceedings of COMPSTAT'2010, pages 155-164, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2604-3_14
    DOI: 10.1007/978-3-7908-2604-3_14
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