Bandwidth selection for smooth backfitting in additive models
Abstract
The smooth backfitting introduced by Mammen, Linton and Nielsen [Ann. Statist. 27 (1999) 1443-1490] is a promising technique to fit additive regression models and is known to achieve the oracle efficiency bound. In this paper, we propose and discuss three fully automated bandwidth selection methods for smooth backfitting in additive models. The first one is a penalized least squares approach which is based on higher-order stochastic expansions for the residual sums of squares of the smooth backfitting estimates. The other two are plug-in bandwidth selectors which rely on approximations of the average squared errors and whose utility is restricted to local linear fitting. The large sample properties of these bandwidth selection methods are given. Their finite sample properties are also compared through simulation experiments.
Keywords
Cite
@article{arxiv.math/0507425,
title = {Bandwidth selection for smooth backfitting in additive models},
author = {Enno Mammen and Byeong U. Park},
journal= {arXiv preprint arXiv:math/0507425},
year = {2007}
}
Comments
Published at http://dx.doi.org/10.1214/009053605000000101 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)