eprintid: 20820
rev_number: 13
eprint_status: archive
userid: 2326
dir: disk0/00/02/08/20
datestamp: 2016-06-02 07:14:49
lastmod: 2016-08-08 09:28:03
status_changed: 2016-06-02 07:14:49
type: workingPaper
metadata_visibility: show
creators_name: Mammen, Enno
creators_name: Linton, Oliver
creators_name: Nielsen, Jens Perch
title: The Existence and Asymptotic Properties of a Backfitting Projection Algorithm under Weak Conditions
subjects: 510
divisions: 110400
keywords: Additive models, Alternating projections, Backfitting, Kernel Smoothing, Local Polynomials, Nonparametric Regression
note: Abweichender Titel: Backfitting under weak conditions
abstract: We derive the asymptotic distribution of a new backfitting procedure for estimating the closest additive approximation to a nonparametric regressionfunction. The procedure employs a recent projection interpretation ofpopular kernel estimators provided by Mammen et al. (1997), and theasymptotic theory of our estimators is derived using the theory of additiveprojections reviewed in Bickel et al. (1995). Our procedure achieves thesame bias and variance as the oracle estimator based on knowing the othercomponents, and in this sense improves on the method analyzed in Opsomer andRuppert (1997). We provide 'high level' conditions independent of thesampling scheme. We then verify that these conditions are satisfied in atime series autoregression under weak conditions.
date: 1998-05-08
id_scheme: DOI
id_number: 10.11588/heidok.00020820
schriftenreihe_cluster_id: sr-10a
schriftenreihe_order: 46
ppn_swb: 1657259722
own_urn: urn:nbn:de:bsz:16-heidok-208202
language: eng
bibsort: MAMMENENNOTHEEXISTEN19980508
full_text_status: public
place_of_pub: Heidelberg
pages: 39
citation:   Mammen, Enno ; Linton, Oliver ; Nielsen, Jens Perch  (1998) The Existence and Asymptotic Properties of a Backfitting Projection Algorithm under Weak Conditions.  [Working paper]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/20820/1/beitrag.46.pdf