Applications of Multilevel Structured Additive Regression Models to Insurance Data View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010

AUTHORS

Stefan Lang , Nikolaus Umlauf

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. More... »

PAGES

155-164

References to SciGraph publications

Book

TITLE

Proceedings of COMPSTAT'2010

ISBN

978-3-7908-2603-6
978-3-7908-2604-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-7908-2604-3_14

DOI

http://dx.doi.org/10.1007/978-3-7908-2604-3_14

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023721467


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