Article (Scientific journals)
Nonlinear regression with censored data
Heuchenne, Cédric; Van Keilegom, Ingrid
2007In Technometrics, 49 (1), p. 34-44
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Keywords :
bootstrap; fatigue life data; kernel method; least squares estimation; nonparametric regression; right censoring; survival analysis; bandwidth selection
Abstract :
[en] Suppose that the random vector (X, Y) satisfies the regression model Y = m(X) + sigma(X)epsilon, where m(.) = E(Y vertical bar.) belongs to some parametric class (m(theta)(.):theta is an element of Theta) of regression functions, sigma(2)(.) = var(Y vertical bar.) is unknown, and e is independent of X. The response Y is subject to random right censoring, and the covariate X is completely observed. A new estimation procedure for the true, unknown parameter vector theta(0) is proposed that extends the classical least squares procedure for nonlinear regression to the case where the response is subject to censoring. The consistency and asymptotic normality of the proposed estimator are established. The estimator is compared through simulations with an estimator proposed by Stute in 1999, and both methods are also applied to a fatigue life dataset of strain-controlled materials.
Disciplines :
Mathematics
Author, co-author :
Heuchenne, Cédric ;  Université de Liège - ULiège > HEC - École de gestion de l'ULiège > Statistique appliquée à la gestion et à l'économie
Van Keilegom, Ingrid
Language :
English
Title :
Nonlinear regression with censored data
Publication date :
2007
Journal title :
Technometrics
ISSN :
0040-1706
eISSN :
1537-2723
Publisher :
American Statistical Association
Volume :
49
Issue :
1
Pages :
34-44
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 16 April 2009

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