Boosting for Estimating Spatially Structured Additive Models

Nikolay Robinzonov () and Torsten Hothorn ()
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Nikolay Robinzonov: Ludwig-Maximilians-Universität München, Institut für Statistik
Torsten Hothorn: Ludwig-Maximilians-Universität München, Institut für Statistik

A chapter in Statistical Modelling and Regression Structures, 2010, pp 181-196 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2413-1_10

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DOI: 10.1007/978-3-7908-2413-1_10

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