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Insurance: an R-Program to Model Insurance Data

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Data sets from car insurance companies often have a high-dimensional complex dependency structure. The use of classical statistical methods such as generalized linear models or Tweedie?s compound Poisson model can yield problems in this case. Christmann (2004) proposed a general approach to model the pure premium by exploiting characteristic features of such data sets. In this paper we describe a program to use this approach based on a combination of multinomial logistic regression and [epsilon]-support vector regression from modern statistical machine learning.

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Marcos Marin-Galiano, Andreas Christmann

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Adapt according to the presented license agreement and reference the original author.