$L_2$ boosting in kernel regression

On boosting kernel regression

In this paper we propose a simple multistep regression smoother which is constructed in an iterative manner, by learning the Nadaraya-Watson estimator with L2boosting. We find, in both theoretical analysis and simulation experiments, that the bias converges exponentially fast, and the variance diverges exponentially slow. The first boosting step is analyzed in more detail, giving asymptotic exp...

L2 boosting in kernel regression

In this paper, we investigate the theoretical and empirical properties of L2 boosting with kernel regression estimates as weak learners. We show that each step of L2 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 L2 boosting...

Boosting with the L_2-Loss: Regression and Classification

L 2 Boosting in Kernel Regression 1

In this paper, we investigate the theoretical and empirical properties of L2 boosting with kernel regression estimates as weak learners. We show that each step of L2 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 L2 boosting...

Multistep kernel regression smoothing by boosting

In this paper we propose a simple multistep regression smoother which is constructed in a boosting fashion, by learning the Nadaraya–Watson estimator with L2Boosting. Differently from the usual approach, we do not focus on L2Boosting for ever. Given a kernel smoother as a learner, we explore the boosting capability to build estimators using a finite number of boosting iterations. This approach ...