$L_2$ boosting in kernel regression
Statistics Theory
2009-09-07 v1 Statistics Theory
Abstract
In this paper, we investigate the theoretical and empirical properties of boosting with kernel regression estimates as weak learners. We show that each step of boosting reduces the bias of the estimate by two orders of magnitude, while it does not deteriorate the order of the variance. We illustrate the theoretical findings by some simulated examples. Also, we demonstrate that boosting is superior to the use of higher-order kernels, which is a well-known method of reducing the bias of the kernel estimate.
Cite
@article{arxiv.0909.0833,
title = {$L_2$ boosting in kernel regression},
author = {B. U. Park and Y. K. Lee and S. Ha},
journal= {arXiv preprint arXiv:0909.0833},
year = {2009}
}
Comments
Published in at http://dx.doi.org/10.3150/08-BEJ160 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)