Statistical Science

Boosting Algorithms: Regularization, Prediction and Model Fitting

Peter Bühlmann and Torsten Hothorn

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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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Statist. Sci. Volume 22, Number 4 (2007), 477-505.

First available in Project Euclid: 7 April 2008

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Bühlmann, Peter; Hothorn, Torsten. Boosting Algorithms: Regularization, Prediction and Model Fitting. Statist. Sci. 22 (2007), no. 4, 477--505. doi:10.1214/07-STS242.

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