Bayesian Estimation of Measurement Error Models with Longitudinal Data

被引：0

作者：

Li, Dewang ^{[1
]}

Qiu, Meilan ^{[1
]}

机构：

[1] Univ Huizhou, Sch Math & Big Data, Huizhou, Guangdong, Peoples R China

来源：

PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON ELECTRONIC INDUSTRY AND AUTOMATION (EIA 2017) | 2017年 / 145卷

关键词：

Bayesian; measurement error; longitudinal data; VARIABLES;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, Bayesian inferences for semiparametric measurement error models (MEs) for longitudinal data are investigated. A semiparametric Bayesian approach combining the stick-breaking prior and the Gibbs sampler together with the Metropolis-Hastings algorithm is developed for simulating observations from the posterior distributions and producing the joint Bayesian estimates of unknow parameters and measurement error. We obtain Bayesian estimations of parameters and covariates subject to MEs. Two simulation studies are presented to illustrate our proposed methodologies.

引用

页码：242 / 245

页数：4

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