Article citationsMore>>

Gamerman, D. (1997) Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference. Chapman and Hall, London.

has been cited by the following article:

• TITLE: Estimations of Weibull-Geometric Distribution under Progressive Type II Censoring Samples

  AUTHORS: Azhari A. Elhag, Omar I. O. Ibrahim, Mohamed A. El-Sayed, Gamal A. Abd-Elmougod

  KEYWORDS: Weibull-Geometric Distribution, Progressive Type II Censoring Samples, Bayesian Estimation, Maximum Likelihood Estimation, Bootstrap Confidence Intervals, Markov Chain Monte Carlo

  JOURNAL NAME: Open Journal of Statistics, Vol.5 No.7, December 18, 2015

  ABSTRACT: This paper deals with the Bayesian inferences of unknown parameters of the progressively Type II censored Weibull-geometric (WG) distribution. The Bayes estimators cannot be obtained in explicit forms of the unknown parameters under a squared error loss function. The approximate Bayes estimators will be computed using the idea of Markov Chain Monte Carlo (MCMC) method to generate from the posterior distributions. Also the point estimation and confidence intervals based on maximum likelihood and bootstrap technique are also proposed. The approximate Bayes estimators will be obtained under the assumptions of informative and non-informative priors are compared with the maximum likelihood estimators. A numerical example is provided to illustrate the proposed estimation methods here. Maximum likelihood, bootstrap and the different Bayes estimates are compared via a Monte Carlo Simulation study