Volume 10 Number 4 (Oct. 2020)
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IJAPM 2020 Vol.10(4): 160-166 ISSN: 2010-362X
DOI: 10.17706/ijapm.2020.10.4.160-166

Bayesian Estimation Analysis of Bernoulli Measurement Error Model for Longitudinal Data

Dewang Li, Meilan Qiu, Zhongyi Ke

Abstract—The Bayesian method is used to study the inference of the semi-parametric measurement error model (MEs) with longitudinal data. A semi-parametric Bayesian method combined with fracture prior and Gibbs sampling combined with Metropolis-Hastings (MH) algorithm is applied and applied to the simulation observation from the posterior distribution, and the combined Bayesian statistics of unknown parameters and measurement errors are obtained. We obtained Bayesian estimates of the parameters and covariates of the measurement error model. Under three different priori assumptions, four simulation studies illustrate the effectiveness and utility of the proposed method.

Index Terms—Bayesian, measurement error, longitudinal data, Bernoulli model.

Dewang Li, Meilan Qiu, Zhongyi Ke are with School of Mathematics and Statistics, Huizhou University, Huizhou, Guangdong, 516007, China.

Cite:Dewang Li, Meilan Qiu, Zhongyi Ke, "Bayesian Estimation Analysis of Bernoulli Measurement Error Model for Longitudinal Data," International Journal of Applied Physics and Mathematics vol. 10, no. 4, pp. 160-166, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

ISSN: 2010-362X (Online)
Abbreviated Title: Int. J. Appl. Phys. Math.
Frequency: Quarterly
DOI: 10.17706/IJAPM
Editor-in-Chief: Prof. Haydar Akca 
Abstracting/ Indexing: INSPEC(IET), CNKI, Google Scholar, EBSCO, Chemical Abstracts Services (CAS), etc.
E-mail: ijapm@iap.org
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