Reference : Location estimation in nonparametric regression with censored data
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Mathematics
http://hdl.handle.net/2268/11274
Location estimation in nonparametric regression with censored data
English
Heuchenne, Cédric mailto [Université de Liège - ULg > HEC - Ecole de gestion de l'ULg > Statistique appliquée à la gestion et à l'économie >]
Van Keilegom, Ingrid [> > > >]
2007
Journal Of Multivariate Analysis
Elsevier Inc
98
8
1558-1582
Yes (verified by ORBi)
International
0047-259X
San Diego
[en] kernel estimation ; location-scale model ; nonparametric regression ; survival analysis ; bandwidth censored regression
[en] Consider the heteroscedastic model Y =m (X) +sigma(X)epsilon, where epsilon and X are independent, Y is subject to right censoring, m (center dot) is an unknown but smooth location function (like e.g. conditional mean, median, trimmed mean...) and sigma(center dot) an unknown but smooth scale function. In this paper we consider the estimation of m(center dot) under this model. The estimator we propose is a Nadaraya-Watson type estimator, for which the censored observations are replaced by 'synthetic' data points estimated under the above model. The estimator offers an alternative for the completely nonparametric estimator of m (center dot), which cannot be estimated consistently in a completely nonparametric way, whenever high quantiles of the conditional distribution of Y given X = x are involved. We obtain the asymptotic properties of the proposed estimator of m (x) and study its finite samplebehaviour in a simulation study. The method is also applied to a study of quasars in astronomy. (c) 2007 Elsevier Inc. All rights reserved.
http://hdl.handle.net/2268/11274
10.1016/j.jmva.2007.03.008

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
mainLart5.pdfPublisher postprint308.03 kBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.