Model-based boosting in R: a hands-on tutorial using the R package mboost

Benjamin Hofner (), Andreas Mayr, Nikolay Robinzonov and Matthias Schmid

Computational Statistics, 2014, vol. 29, issue 1, 3-35

Abstract: We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates as base-learners for fitting various kinds of generalized linear and generalized additive models to potentially high-dimensional data. We give a theoretical background and demonstrate how mboost can be used to fit interpretable models of different complexity. As an example we use mboost to predict the body fat based on anthropometric measurements throughout the tutorial. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Boosting; Component-wise functional gradient descent; Generalized additive models; Tutorial (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (23)

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DOI: 10.1007/s00180-012-0382-5

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