1Foundation Program Unit, University of Doha for Science and Technology, Doha, Qatar
2Department of Statistics, Yarmouk University, Irbid, Jordan
International Journal of Business and Risk Management.
2025,
Vol. 6 No. 1, 11-18
DOI: 10.12691/ijbrm-6-1-1
Copyright © 2025 Science and Education PublishingCite this paper: Wassim Abou Ghaida, Ayman Baklizi. Parameter Estimation and Bayesian Prediction in the Log-logistic Distribution under Type-II Censored Data.
International Journal of Business and Risk Management. 2025; 6(1):11-18. doi: 10.12691/ijbrm-6-1-1.
Correspondence to: Wassim Abou Ghaida, Foundation Program Unit, University of Doha for Science and Technology, Doha, Qatar. Email:
wassim.aboughaida@udst.edu.qaAbstract
We consider Bayesian inference and point prediction in log-logistic distribution based on type-II censored data. We assume that the scale and shape parameters have independent gamma priors. The Bayes estimators cannot be obtained in closed form; therefore, we use Metropolis Hasting algorithm to approximate the Bayes estimates of the unknown scale and shape parameters. We compare the performance of the Bayes estimator with the Maximum Likelihood estimators. In addition, we obtained the Bayesian credibility intervals and compared them with the Wald intervals. Moreover, we derived Bayesian point and interval predictors for future observation and investigated their performance using simulation techniques. And finally, we analysed real data set for illustration purposes.
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