Bayesian Estimation of Measurement Error Models with Longitudinal Data
- 10.2991/eia-17.2017.52How to use a DOI?
- Bayesian; measurement error; longitudinal data
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.
- © 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/).
Cite this article
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 -