Christmann, AndreasMarin-Galiano, Marcos2004-12-062004-12-062004http://hdl.handle.net/2003/530110.17877/DE290R-6724Data 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.enUniversität Dortmundclaim sizeinsurance tarifflogistic regressionstatistical machine learningsupport vector regression310report