Recursive Bias Estimation and $L_2$ Boosting
| dc.creator | Cornillon, Pierre Andre | |
| dc.creator | Hengartner, Nicolas | |
| dc.creator | Matzner-Lober, Eric | |
| dc.date | 2008-01-30 | |
| dc.date.accessioned | 2026-07-07T08:57:16Z | |
| dc.date.available | 2026-07-07T08:57:16Z | |
| dc.description | 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. | |
| dc.identifier | https://arxiv.org/abs/0801.4629 | |
| dc.identifier | http://arxiv.org/abs/0801.4629 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/146903 | |
| dc.subject | Methodology | |
| dc.subject | Machine Learning | |
| dc.title | Recursive Bias Estimation and $L_2$ Boosting | |
| dc.type | text |