Simultaneous Selection of Variables and Smoothing Parameters in Additive Models

  • Rüdiger Krause
  • Gerhard Tutz
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


For additive models of the type y = f1(x1) + … + fP(xp) + ε where fj,j = 1, …, p, have unspecified functional form the problem of variable selection is strongly connected to the choice of the amount of smoothing used for components fj. In this paper we propose the simultaneous choice of variables and smoothing parameters based on genetic algorithms. Common genetic algorithms have to be modified since inclusion of variables and smoothing have to be coded separately but are linked in the search for optimal solutions. The basic tool for fitting the additive model is the expansion in B-splines. This approach allows for direct estimates which is essential for the method to work.


Genetic Algorithm Variable Selection Smoothing Parameter Parent String Parameter String 


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Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Rüdiger Krause
    • 1
  • Gerhard Tutz
    • 1
  1. 1.Institut für StatistikLudwig-Maximilians Universität MünchenMünchenGermany

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