doi: 10.17706/ijapm.2019.9.4.167-172
Semiparametric Bayesian Estimation of Bernoulli Model with Measurement Error
,
Meilan Qiu
,
Yuanying Zhao
- School of Mathematics and Big Data, Huizhou University, Huizhou, Guangdong, 516007, China
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.
Keywords
- Monte Carlo
- Bayesian estimation
- Bernoulli model
- measurement error
How to Cite
Dewang Li, Meilan Qiu, and 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. https://doi.org/ 10.17706/ijapm.2019.9.4.167-172
Copyright & License
Copyright © 2019 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).