Bayesian Estimation of Measurement Error Models with Longitudinal Data

DOI

10.2991/eia-17.2017.52How to use a DOI?

Keywords

Bayesian; measurement error; longitudinal data

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dewang LI
AU  - Meilan QIU
PY  - 2017/07
DA  - 2017/07
TI  - Bayesian Estimation of Measurement Error Models with Longitudinal Data
BT  - Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)
PB  - Atlantis Press
SP  - 242
EP  - 245
SN  - 1951-6851
UR  - https://doi.org/10.2991/eia-17.2017.52
DO  - 10.2991/eia-17.2017.52
ID  - LI2017/07
ER  -