Bayesian Prediction Intervals Based on Progressive Type-II Censored Sample from a General Class of Distributions | ||
| Al-Azhar Bulletin of Science | ||
| Articles in Press, Accepted Manuscript, Available Online from 12 July 2025 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/absb.2025.441164 | ||
| Authors | ||
| Salwa Reda Abdou* 1; Mostafa Mohamed Mohie El-Din2; Amr Fouad Sadek2 | ||
| 1Dept. of Math., Faculty of Science, Fayoum University, Fayoum, Egypt. | ||
| 2Dept. of Math., Faculty of Science, Al-Azhar University, Nasr City, Cairo, Egypt. | ||
| Abstract | ||
| In this paper, we consider a general class of distributions for constructing one and two-sample Bayesian prediction intervals assuming a progressive Type-II censored sample. Approximate predictive survival functions are obtained using importance sampling and Markov Chain Monte Carlo (MCMC) techniques. The resulting outcomes are illustrated using examples from the inverse Weibull, inverted exponential, and Gompertz distributions. Finally, several real-life data sets are presented to demonstrate the conclusions reached here. | ||
| Keywords | ||
| Bayesian prediction; Gompertz distribution; Importance sampling technique; Inverse Weibull distribution; Markov Chain Monte Carlo technique; Progressive Type-II censored | ||
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