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Bandwidth selection for smooth backfitting in additive models (2005)
| Venue: | Annals of Statistics |
| Citations: | 17 - 9 self |
BibTeX
@ARTICLE{Mammen05bandwidthselection,
author = {Enno Mammen and Byeong U. Park},
title = {Bandwidth selection for smooth backfitting in additive models},
journal = {Annals of Statistics},
year = {2005},
pages = {1260--1294}
}
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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. 1. Introduction. Nonparametric
Keyphrases
smooth backfitting additive model bandwidth selection bandwidth selection method nielsen ann additive regression model higher-order stochastic expansion smooth backfitting estimate residual sum local linear fitting finite sample property first one oracle efficiency bound penalized least square simulation experiment promising technique plug-in bandwidth selector large sample property
