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Generalized Linear Mixed Models Based on Boosting

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Statistical Modelling and Regression Structures

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.

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Author information

Authors and Affiliations

  1. Institut für Statistik, Ludwig-Maximilians-Universität München, Ludwigstraße 33, München, Germany

    Gerhard Tutz

  2. Institut für Statistik, Ludwig-Maximilians-Universität München, München, Germany

    Andreas Groll

Authors
  1. Gerhard Tutz
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  2. Andreas Groll
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Correspondence to Gerhard Tutz .

Editor information

Editors and Affiliations

  1. Fak. V - Mathematik und, Universität Oldenburg, Ammerländer Heerstr. 114-118, Oldenburg, 26129, Germany

    Thomas Kneib

  2. Institut für Statistik, Universität München, Akademiestr. 1, München, 80799, Germany

    Gerhard Tutz

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Tutz, G., Groll, A. (2010). Generalized Linear Mixed Models Based on Boosting. In: Kneib, T., Tutz, G. (eds) Statistical Modelling and Regression Structures. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2413-1_11

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