Abstract
We present a statistical perspective on boosting. Special emphasis
is given to estimating potentially complex parametric or nonparametric
models, including generalized linear and additive models as well as regression
models for survival analysis. Concepts of degrees of freedom and corresponding
Akaike or Bayesian information criteria, particularly useful for
regularization and variable selection in high-dimensional covariate spaces,
are discussed as well.
The practical aspects of boosting procedures for fitting statistical models
are illustrated by means of the dedicated open-source software package
mboost. This package implements functions which can be used for model fitting,
prediction and variable selection. It is flexible, allowing for the implementation
of new boosting algorithms optimizing user-specified loss functions.
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