Sieve MLE for Generalized Partial Linear Models with Type Ⅱ Interval-censored Data  

作　　者：王晓光 宋立新 

机构地区：[1]Department of Applied Mathematics, Dalian University of Technology, Dalian, 116024

出　　处：《Northeastern Mathematical Journal》2008年第2期150-162,共13页东北数学（英文版）

基　　金：The talent research fund launched (3004-893325) of Dalian University of Technology;the NNSF (10271049) of China.

摘　　要：This article concerded with a semiparametric generalized partial linear model （GPLM） with the type Ⅱ censored data. A sieve maximum likelihood estimator （MLE） is proposed to estimate the parameter component, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically normal and efficient, and the estimator of the nonparametric function has an optimal convergence rate.This article concerded with a semiparametric generalized partial linear model （GPLM） with the type Ⅱ censored data. A sieve maximum likelihood estimator （MLE） is proposed to estimate the parameter component, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically normal and efficient, and the estimator of the nonparametric function has an optimal convergence rate.

关 键 词：generalized partial linear model Sieve maximum likelihood estimator strongly consistent optimal convergence rate asymptotically efficient estimator 

分 类 号：O212.7[理学—概率论与数理统计]