Insurance: an R-Program to Model Insurance Data
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Date
2004
Journal Title
Journal ISSN
Volume Title
Publisher
Universität Dortmund
Abstract
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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Keywords
claim size, insurance tariff, logistic regression, statistical machine learning, support vector regression