Volume 9 Number 4 (Oct. 2019)
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IJAPM 2019 Vol.9(4): 167-172 ISSN: 2010-362X
doi: 10.17706/ijapm.2019.9.4.167-172

Semiparametric Bayesian Estimation of Bernoulli Model with Measurement Error

Dewang Li, Meilan Qiu, Yuanying Zhao
Abstract—In this paper, we relax the fully parametric distributional assumption of measurement errors (MEs) to establish mixture Bernoulli model by a centered Dirichlet process. A hybrid algorithm is presented to generate observations required for a Bayesian inference from the posterior distributions of parameters and covariates subject to MEs in Bernoulli model by combining the stick-breaking prior and the Gibbs sampler together with the Metropolis-Hastings algorithm. Two Monte Carlo studies illustrate the superiority of the measurement error estimators in certain situations.

Index Terms—Monte Carlo, Bayesian estimation, Bernoulli model, measurement error.

Dewang Li, Meilan Qiu are with School of Mathematics and Big Data, Huizhou University, Huizhou, Guangdong, 516007, China (email: qml_1981@126.com).
Yuanying Zhao is with College of Mathematics and Information science, Guiyang University, Guizhou, 550005, China (zhaoyuanying_@126.com).

Cite: Dewang Li, Meilan Qiu, Yuanying Zhao, "Semiparametric Bayesian Estimation of Bernoulli Model with Measurement Error," International Journal of Applied Physics and Mathematics vol. 9, no. 4, pp. 167-172, 2019.

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:  Index Copernicus, EI (INSPEC, IET), Chemical Abstracts Services (CAS), Nanowerk Database, Google Scholar, EBSCO, etc.
E-mail: ijapm@iap.org
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