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Generalized Linear Mixed Models Based on Boosting

In: Statistical Modelling and Regression Structures

Author

Listed:
  • Gerhard Tutz

    (Ludwig-Maximilians-Universität München, Institut für Statistik)

  • Andreas Groll

    (Ludwig-Maximilians-Universität München, Institut für Statistik)

Abstract

A likelihood-based boosting approach for fitting generalized linear mixed models is presented. In contrast to common procedures it can be used in highdimensional settings where a large number of potentially influential explanatory variables is available. Constructed as a componentwise boosting method it is able to perform variable selection with the complexity of the resulting estimator being determined by information criteria. The method is investigated in simulation studies and illustrated by using a real data set.

Suggested Citation

  • Gerhard Tutz & Andreas Groll, 2010. "Generalized Linear Mixed Models Based on Boosting," Springer Books, in: Thomas Kneib & Gerhard Tutz (ed.), Statistical Modelling and Regression Structures, pages 197-215, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2413-1_11
    DOI: 10.1007/978-3-7908-2413-1_11
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