This article considers a semiparametric varying-coefficient partially linear regression model.The semiparametric varying-coefficient partially linear regression model which is a generalization of the partially linear ...This article considers a semiparametric varying-coefficient partially linear regression model.The semiparametric varying-coefficient partially linear regression model which is a generalization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable.A sieve M-estimation method is proposed and the asymptotic properties of the proposed estimators are discussed.Our main object is to estimate the nonparametric component and the unknown parameters simultaneously.It is easier to compute and the required computation burden is much less than the existing two-stage estimation method.Furthermore,the sieve M-estimation is robust in the presence of outliers if we choose appropriate ρ(·).Under some mild conditions,the estimators are shown to be strongly consistent;the convergence rate of the estimator for the unknown nonparametric component is obtained and the estimator for the unknown parameter is shown to be asymptotically normally distributed.Numerical experiments are carried out to investigate the performance of the proposed method.展开更多
This paper discusses efficient estimation for the additive hazards regression model when only bi- variate current status data are available.Current status data occur in many fields including demographical studies and ...This paper discusses efficient estimation for the additive hazards regression model when only bi- variate current status data are available.Current status data occur in many fields including demographical studies and tumorigenicity experiments (Keiding,1991;Sun,2006) and several approaches have been proposed for the additive hazards model with univariate current status data (Lin et al.,1998;Martinussen and Scheike,2002).For bivariate data,in addition to facing the same problems as those with univariate data,one needs to deal with the association or correlation between two related failure time variables of interest.For this,we employ the copula model and an efficient estimation procedure is developed for inference.Simulation studies are performed to evaluate the proposed estimates and suggest that the approach works well in practical situations.An illustrative example is provided.展开更多
基金supported by Natural Natural Science Foundation of China (Grant Nos.10771017,10901020)Key Project of Chinese Ministry of Education (Grant No.309007)
文摘This article considers a semiparametric varying-coefficient partially linear regression model.The semiparametric varying-coefficient partially linear regression model which is a generalization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable.A sieve M-estimation method is proposed and the asymptotic properties of the proposed estimators are discussed.Our main object is to estimate the nonparametric component and the unknown parameters simultaneously.It is easier to compute and the required computation burden is much less than the existing two-stage estimation method.Furthermore,the sieve M-estimation is robust in the presence of outliers if we choose appropriate ρ(·).Under some mild conditions,the estimators are shown to be strongly consistent;the convergence rate of the estimator for the unknown nonparametric component is obtained and the estimator for the unknown parameter is shown to be asymptotically normally distributed.Numerical experiments are carried out to investigate the performance of the proposed method.
基金partly supported by National Natural Science Foundation of China (Grant No. 10971015, 11131002)Key Project of Chinese Ministry of Education (Grant No. 309007)the Fundamental Research Funds for the Central Universities
文摘This paper discusses efficient estimation for the additive hazards regression model when only bi- variate current status data are available.Current status data occur in many fields including demographical studies and tumorigenicity experiments (Keiding,1991;Sun,2006) and several approaches have been proposed for the additive hazards model with univariate current status data (Lin et al.,1998;Martinussen and Scheike,2002).For bivariate data,in addition to facing the same problems as those with univariate data,one needs to deal with the association or correlation between two related failure time variables of interest.For this,we employ the copula model and an efficient estimation procedure is developed for inference.Simulation studies are performed to evaluate the proposed estimates and suggest that the approach works well in practical situations.An illustrative example is provided.
