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Boosting for Estimating Spatially Structured Additive Models

  • Nikolay Robinzonov
  • Torsten Hothorn
Chapter

Abstract

Spatially structured additivemodels offer the flexibility to estimate regression relationships for spatially and temporally correlated data. Here, we focus on the estimation of conditional deer browsing probabilities in the National Park “Bayerischer Wald”. The models are fitted using a componentwise boosting algorithm. Smooth and non-smooth base learners for the spatial component of the models are compared. A benchmark comparison indicates that browsing intensities may be best described by non-smooth base learners allowing for abrupt changes in the regression relationship.

Keywords

Generalize Additive Model Ensemble Method Generalize Additive Model Model Browse Tree Smooth Relationship 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Institut für StatistikLudwig-Maximilians-Universität MünchenMünchenGermany

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