Applications of Multilevel Structured Additive Regression Models to Insurance Data

Arbeitspapier

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 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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Working Papers in Economics and Statistics ; No. 2010-01

Klassifikation und Themen

• Klassifikation: Wirtschaft
• Thema: Bayesian hierarchical models ; multilevel models ; P-splines ; spatial heterogeneity ; Bayes-Statistik ; Theorie

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Lang, Stefan ; Umlauf, Nikolaus
• Ereignis: Veröffentlichung
• (wer): University of Innsbruck, Department of Public Finance
• (wo): Innsbruck
• (wann): 2010

Weitere Informationen

• Handle: http://hdl.handle.net/10419/73517
• Letzte Aktualisierung: 10.03.2025, 11:45 MEZ