Bayesian inference for the randomly censored Burr-type XII distribution. Issue 2 (25th January 2018)

• Record Type: Journal Article
• Authors: Danish, Muhammad Yameen; Arshad, Irshad Ahmad; Aslam, Muhammad
• Is Part Of: Journal of applied statistics. Volume 45:Issue 2(2018)
• Page Start: 270
• Page End: 283
• Publication Date: 2018-01-25
• Subjects: Log-concave density function -- Gibbs sampling -- Lindley's method -- posterior predictive p-value -- MCMC; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2016.1275530
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: The article presents the Bayesian inference for the parameters of randomly censored Burr-type XII distribution with proportional hazards. The joint conjugate prior of the proposed model parameters does not exist; we consider two different systems of priors for Bayesian estimation. The explicit forms of the Bayes estimators are not possible; we use Lindley's method to obtain the Bayes estimates. However, it is not possible to obtain the Bayesian credible intervals with Lindley's method; we suggest the Gibbs sampling procedure for this purpose. Numerical experiments are performed to check the properties of the different estimators. The proposed methodology is applied to a real-life data for illustrative purposes. The Bayes estimators are compared with the Maximum likelihood estimators via numerical experiments and real data analysis. The model is validated using posterior predictive simulation in order to ascertain its appropriateness.