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Simultaneous Selection of Variables and Smoothing Parameters in Additive Models

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Innovations in Classification, Data Science, and Information Systems

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Abstract

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.

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Author information

Authors and Affiliations

  1. Institut für Statistik, Ludwig-Maximilians Universität München, Akademiestr.l, D-80799, München, Germany

    Rüdiger Krause & Gerhard Tutz

Authors
  1. Rüdiger Krause
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  2. Gerhard Tutz
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Editor information

Editors and Affiliations

  1. Institute of Business Administration and Economics, Brandenburg University of Technology Cottbus, Konrad-Wachsmann-Allee 1, 03046, Cottbus, Germany

    Daniel Baier (Chair of Marketing and Innovation Management) (Chair of Marketing and Innovation Management)

  2. Department of Medical Biometrics Charité Virchow-Klinikum, Humboldt University Berlin, 13344, Berlin, Germany

    Klaus-Dieter Wernecke

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© 2005 Springer-Verlag Berlin · Heidelberg

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Krause, R., Tutz, G. (2005). Simultaneous Selection of Variables and Smoothing Parameters in Additive Models. In: Baier, D., Wernecke, KD. (eds) Innovations in Classification, Data Science, and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26981-9_18

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