Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/101078
Authors: 
Klein, Nadja
Denuit, Michel
Lang, Stefan
Kneib, Thomas
Year of Publication: 
2013
Series/Report no.: 
Working Papers in Economics and Statistics 2013-24
Abstract: 
Generalized additive models for location, scale and shape define a flexible, semi-parametric class of regression models for analyzing insurance data in which the exponential family assumption for the response is relaxed. This approach allows the actuary to include risk factors not only in the mean but also in other parameters governing the claiming behavior, like the degree of residual heterogeneity or the no-claim probability. In this broader setting, the Negative Binomial regression with cell-specific heterogeneity and the zero-inflated Poisson regression with cell-specific additional probability mass at zero are applied to model claim frequencies. Models for claim severities that can be applied either per claim or aggregated per year are also presented. Bayesian inference is based on efficient Markov chain Monte Carlo simulation techniques and allows for the simultaneous estimation of possible nonlinear effects, spatial variations and interactions between risk factors within the data set. To illustrate the relevance of this approach, a detailed case study is proposed based on the Belgian motor insurance portfolio studied in Denuit and Lang (2004).
Subjects: 
overdispersed count data
mixed Poisson regression
zero-inflated Poisson
Negative Binomial
zero-adjusted models
MCMC
probabilistic forecasts
Document Type: 
Working Paper

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