Reference : Polynomial regression with censored data based on preliminary nonparametric estimation
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Mathematics
http://hdl.handle.net/2268/11270
Polynomial regression with censored data based on preliminary nonparametric estimation
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
Annals of the Institute of Statistical Mathematics
Springer Heidelberg
59
2
273-297
Yes (verified by ORBi)
International
0020-3157
Heidelberg
[en] bootstrap ; kernel estimation ; least squares estimation ; linear regression ; nonparametric regression ; right censoring ; survival analysis ; bandwidth
[en] Consider the polynomial regression model Y = (beta)0 + beta(1) X + center dot center dot center dot beta X-p(p) + sigma (X)epsilon, where sigma(2)(X) = Var(Y vertical bar X) is unknown, and epsilon is independent of X and has zero mean. Suppose that Y is subject to random right censoring. A new estimation procedure for the parameters beta(0), center dot center dot center dot, beta (p) is proposed, which extends the classical least squares procedure to censored data. The proposed method is inspired by the method of Buckley and James (1979, Biometrika, 66, 429-436), but is, unlike the latter method, a noniterative procedure due to nonparametric preliminary estimation of the conditional regression function. The asymptotic normality of the estimators is established. Simulations are carried out for both methods and they show that the proposed estimators have usually smaller variance and smaller mean squared error than Buckley-James estimators. The two estimation procedures are also applied to a medical and a astronomical data set.
http://hdl.handle.net/2268/11270
10.1007/s10463-006-0066-4

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
mainLart1.pdfNo commentaryPublisher postprint234.13 kBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.