Estimation in the semiparametric frailty model with covariate measurement errors

被引：0
|
作者：
Liu Huanbin [1 ,2 ]
Sun Liuquan [3 ]
机构：
[1] Huazhong Univ Sci & Technol, Sch Math & Stat, Wuhan 430074, Hubei, Peoples R China
[2] Huanggang Normal Univ, Dept Math, Huanggang, Hubei, Peoples R China
[3] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
来源：
2009 IITA INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS ENGINEERING, PROCEEDINGS | 2009年
关键词：
Censored Survival data; Frailty models; General parameter families; Markov chain Monte Carlo methods; covariate measurement errors; Stochastic approximation; STOCHASTIC-APPROXIMATION; REGRESSION-MODELS;
D O I：
10.1109/CASE.2009.57
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Marginal partial likelihood approach is used for estimating the parameters in general frailty measurement error models when covariates are measured with error. An efficient algorithm based on Markov chain Monte Carlo stochastic approximation is proposed to solve the resulting estimating equations. Simulation studies show that the proposed estimation procedure work well and gives accurate estimates and their variance estimates. We also illustrate the method with a data set from the western Kenya parasitaemia data.
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页码：546 / +
页数：2
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