DMCA
Boosting algorithms: Regularization, prediction and model fitting (2007)
Cached
Download Links
| Venue: | Statistical Science |
| Citations: | 99 - 12 self |
BibTeX
@ARTICLE{Bühlmann07boostingalgorithms:,
author = {Peter Bühlmann and Torsten Hothorn},
title = {Boosting algorithms: Regularization, prediction and model fitting},
journal = {Statistical Science},
year = {2007},
pages = {477--505}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
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 highdimensional 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. Key words and phrases: Generalized linear models, generalized additive models, gradient boosting, survival analysis, variable selection, software. 1.
Keyphrases
model fitting variable selection survival analysis additive model nonparametric model linear model bayesian information criterion key word gradient boosting statistical model statistical perspective special emphasis package implement function regression model boosting procedure user-specified loss function complex parametric highdimensional covariate space practical aspect open-source software package mboost
