  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.
