Bayesian Analysis Using Joint Progressive Type-II Censoring Scheme
The joint censoring technique becomes crucial when the study’s aim is to assess the comparative advantages of products concerning their service times. In recent years, there has been a growing interest in progressive censoring as a means to reduce both cost and experiment duration. This article delv...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/15/10/1884 |
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| author | Mohamed G. M. Ghazal Mustafa M. Hasaballah Rashad M. EL-Sagheer Oluwafemi Samson Balogun Mahmoud E. Bakr |
| author_facet | Mohamed G. M. Ghazal Mustafa M. Hasaballah Rashad M. EL-Sagheer Oluwafemi Samson Balogun Mahmoud E. Bakr |
| author_sort | Mohamed G. M. Ghazal |
| collection | DOAJ |
| description | The joint censoring technique becomes crucial when the study’s aim is to assess the comparative advantages of products concerning their service times. In recent years, there has been a growing interest in progressive censoring as a means to reduce both cost and experiment duration. This article delves into the realm of statistical inference for the three-parameter Burr-XII distribution using a joint progressive Type II censoring approach applied to two separate samples. We explore both maximum likelihood and Bayesian methods for estimating model parameters. Furthermore, we derive approximate confidence intervals based on the observed information matrix and employ four bootstrap methods to obtain confidence intervals. Bayesian estimators are presented for both symmetric and asymmetric loss functions. Since closed-form solutions for Bayesian estimators are unattainable, we resort to the Markov chain Monte Carlo method to compute these estimators and the corresponding credible intervals. To assess the performance of our estimators, we conduct extensive simulation experiments. Finally, to provide a practical illustration, we analyze a real dataset. |
| first_indexed | 2024-03-10T20:51:35Z |
| format | Article |
| id | doaj.art-376346b739174b6da725b2a461b02a89 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T20:51:35Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-376346b739174b6da725b2a461b02a892023-11-19T18:18:13ZengMDPI AGSymmetry2073-89942023-10-011510188410.3390/sym15101884Bayesian Analysis Using Joint Progressive Type-II Censoring SchemeMohamed G. M. Ghazal0Mustafa M. Hasaballah1Rashad M. EL-Sagheer2Oluwafemi Samson Balogun3Mahmoud E. Bakr4Department of Mathematics, Faculty of Science, Minia University, Minia 61519, EgyptMarg Higher Institute of Engineering and Modern Technology, Cairo 11721, EgyptMathematics Department, Faculty of Science, Al-Azhar University, Cairo 11884, EgyptDepartment of Computing, University of Eastern Finland, FI-70211 Kuopio, FinlandDepartment of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi ArabiaThe joint censoring technique becomes crucial when the study’s aim is to assess the comparative advantages of products concerning their service times. In recent years, there has been a growing interest in progressive censoring as a means to reduce both cost and experiment duration. This article delves into the realm of statistical inference for the three-parameter Burr-XII distribution using a joint progressive Type II censoring approach applied to two separate samples. We explore both maximum likelihood and Bayesian methods for estimating model parameters. Furthermore, we derive approximate confidence intervals based on the observed information matrix and employ four bootstrap methods to obtain confidence intervals. Bayesian estimators are presented for both symmetric and asymmetric loss functions. Since closed-form solutions for Bayesian estimators are unattainable, we resort to the Markov chain Monte Carlo method to compute these estimators and the corresponding credible intervals. To assess the performance of our estimators, we conduct extensive simulation experiments. Finally, to provide a practical illustration, we analyze a real dataset.https://www.mdpi.com/2073-8994/15/10/1884joint progressive censoring schemethree-parameter Burr-XII distributionmaximum likelihood estimatorsparametric bootstrapMarkov chain Monte Carlo method |
| spellingShingle | Mohamed G. M. Ghazal Mustafa M. Hasaballah Rashad M. EL-Sagheer Oluwafemi Samson Balogun Mahmoud E. Bakr Bayesian Analysis Using Joint Progressive Type-II Censoring Scheme Symmetry joint progressive censoring scheme three-parameter Burr-XII distribution maximum likelihood estimators parametric bootstrap Markov chain Monte Carlo method |
| title | Bayesian Analysis Using Joint Progressive Type-II Censoring Scheme |
| title_full | Bayesian Analysis Using Joint Progressive Type-II Censoring Scheme |
| title_fullStr | Bayesian Analysis Using Joint Progressive Type-II Censoring Scheme |
| title_full_unstemmed | Bayesian Analysis Using Joint Progressive Type-II Censoring Scheme |
| title_short | Bayesian Analysis Using Joint Progressive Type-II Censoring Scheme |
| title_sort | bayesian analysis using joint progressive type ii censoring scheme |
| topic | joint progressive censoring scheme three-parameter Burr-XII distribution maximum likelihood estimators parametric bootstrap Markov chain Monte Carlo method |
| url | https://www.mdpi.com/2073-8994/15/10/1884 |
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