Mixtures of Semiparametric Varying Coefficient Models for Longitudinal Data with Nonignorable Dropout  

作　　者：Zhi-qiang Li Liu-gen Xue 

机构地区：[1]College of Sciences, Beijing University of Chemical Technology Beijing 100029, China [2]College of Applied Sciences, Beijing University of Technology Beijing 100022, China

出　　处：《Acta Mathematicae Applicatae Sinica》2010年第1期125-132,共8页应用数学学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No.10571008)

摘　　要：Informative dropout often arise in longitudinal data. In this paper we propose a mixture model in which the responses follow a semiparametric varying coefficient random effects model and some of the regression coefficients depend on the dropout time in a non-parametric way. The local linear version of the profile-kernel method is used to estimate the parameters of the model. The proposed estimators are shown to be consistent and asymptotically normal, and the finite performance of the estimators is evaluated by numerical simulation.Informative dropout often arise in longitudinal data. In this paper we propose a mixture model in which the responses follow a semiparametric varying coefficient random effects model and some of the regression coefficients depend on the dropout time in a non-parametric way. The local linear version of the profile-kernel method is used to estimate the parameters of the model. The proposed estimators are shown to be consistent and asymptotically normal, and the finite performance of the estimators is evaluated by numerical simulation.

关 键 词：Nonignorable dropout  Estimating equation  Profile-kernel  Local linear estimation  Longitudinal data Semiparametric varying coefficient 

分 类 号：O212.7[理学—概率论与数理统计] O212[理学—数学]