Recursive Bias Estimation and $L_2$ Boosting
Methodology
2008-01-31 v1 Machine Learning
Abstract
This paper presents a general iterative bias correction procedure for regression smoothers. This bias reduction schema is shown to correspond operationally to the Boosting algorithm and provides a new statistical interpretation for Boosting. We analyze the behavior of the Boosting algorithm applied to common smoothers which we show depend on the spectrum of . 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.
Cite
@article{arxiv.0801.4629,
title = {Recursive Bias Estimation and $L_2$ Boosting},
author = {Pierre Andre Cornillon and Nicolas Hengartner and Eric Matzner-Lober},
journal= {arXiv preprint arXiv:0801.4629},
year = {2008}
}