Boosting algorithms: Regularization, prediction and model fitting
Boosting algorithms: Regularization, prediction and model fitting
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DOI：
10.1214/07-sts242
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发表时间：
2007-11-01
影响因子：
5.7
通讯作者：
Hothorn, Torsten
中科院分区：
文献类型：
--
作者：
Buehlmann, Peter;Hothorn, Torsten
我们提出了一种关于助推的统计观点。特别强调估计潜在复杂的参数或非参数模型，包括广义线性模型和加性模型以及用于生存分析的回归模型。文中还讨论了自由度的概念和相应的Akaike或Bayes信息准则，这些准则对于高维协变量空间中的正则化和变量选择特别有用，并通过专用开源软件mBoost说明了用于拟合统计模型的Boost过程的实用方面。该程序实现了模型拟合、预测和变量选择等功能。它是灵活的，允许实施新的提升算法来优化用户指定的损失函数。
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.