摘要翻译：
本文提出了一种通用的回归平滑器迭代偏差校正程序。该偏差减少模式在操作上与$L_2$Boosting算法相对应，并为$L_2$Boosting提供了一个新的统计解释。我们分析了Boosting算法应用于常见平滑器$S$的行为，我们发现它依赖于$I-S$的频谱。我们给出了普通平滑器的例子，对于这些平滑器，Boosting产生发散序列。统计解释建议将算法与适当的停止规则结合起来进行迭代过程。最后通过仿真研究说明了迭代平滑器的有限样本性能。模拟。
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英文标题：
《Recursive Bias Estimation and $L_2$ Boosting》
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作者：
Pierre Andre Cornillon, Nicolas Hengartner, Eric Matzner-Lober
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最新提交年份：
2008
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  This paper presents a general iterative bias correction procedure for regression smoothers. This bias reduction schema is shown to correspond operationally to the $L_2$ Boosting algorithm and provides a new statistical interpretation for $L_2$ Boosting. We analyze the behavior of the Boosting algorithm applied to common smoothers $S$ which we show depend on the spectrum of $I-S$. We present examples of common smoother for which Boosting generates a divergent sequence. The statistical interpretation suggest combining algorithm with an appropriate stopping rule for the iterative procedure. Finally we illustrate the practical finite sample performances of the iterative smoother via a simulation study. simulations.
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PDF链接：
https://arxiv.org/pdf/801.4629