TY - JOUR
T1 - Local linear regression in proportional hazards model with censored data
AU - Zhao, Xiaobing
AU - Zhou, Xian
AU - Wu, Xianyi
PY - 2007/1
Y1 - 2007/1
N2 - In this article we study the method of nonparametric regression based on a transformation model, under which an unknown transformation of the survival time is nonlinearly, even more, nonparametrically, related to the covariates with various error distributions, which are parametrically specified with unknown parameters. Local linear approximations and locally weighted least squares are applied to obtain estimators for the effects of covariates with censored observations. We show that the estimators are consistent and asymptotically normal. This transformation model, coupled with local linear approximation techniques, provides many alternatives to the more general proportional hazards models with nonparametric covariates.
AB - In this article we study the method of nonparametric regression based on a transformation model, under which an unknown transformation of the survival time is nonlinearly, even more, nonparametrically, related to the covariates with various error distributions, which are parametrically specified with unknown parameters. Local linear approximations and locally weighted least squares are applied to obtain estimators for the effects of covariates with censored observations. We show that the estimators are consistent and asymptotically normal. This transformation model, coupled with local linear approximation techniques, provides many alternatives to the more general proportional hazards models with nonparametric covariates.
UR - http://www.scopus.com/inward/record.url?scp=36049009687&partnerID=8YFLogxK
U2 - 10.1080/03610920701386828
DO - 10.1080/03610920701386828
M3 - Article
AN - SCOPUS:36049009687
VL - 36
SP - 2761
EP - 2776
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
SN - 0361-0926
IS - 15
ER -