Bayesian and non-bayesian analysis for stress-strength model based on progressively first failure censoring with applications.
This article examines the estimate of ϑ = P [T < Q], using both Bayesian and non-Bayesian methods, utilizing progressively first-failure censored data. Assume that the stress (T) and strength (Q) are independent random variables that follow the Burr III distribution and the Burr XII distribution,...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0312937 |
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| author | Salem A Alyami Amal S Hassan Ibrahim Elbatal Olayan Albalawi Mohammed Elgarhy Ahmed R El-Saeed |
| author_facet | Salem A Alyami Amal S Hassan Ibrahim Elbatal Olayan Albalawi Mohammed Elgarhy Ahmed R El-Saeed |
| author_sort | Salem A Alyami |
| collection | DOAJ |
| description | This article examines the estimate of ϑ = P [T < Q], using both Bayesian and non-Bayesian methods, utilizing progressively first-failure censored data. Assume that the stress (T) and strength (Q) are independent random variables that follow the Burr III distribution and the Burr XII distribution, respectively, with a common first-shape parameter. The Bayes estimator and maximum likelihood estimator of ϑ are obtained. The maximum likelihood (ML) estimator is obtained for non-Bayesian estimation, and the accompanying confidence interval is constructed using the delta approach and the asymptotic normality of ML estimators. Through the use of non-informative and gamma informative priors, the Bayes estimator of ϑ under squared error and linear exponential loss functions is produced. It is suggested that Markov chain Monte Carlo techniques be used for Bayesian estimation in order to achieve Bayes estimators and the associated credible intervals. To evaluate the effectiveness of the several estimators created, a Monte Carlo numerical analysis is also carried out. In the end, for illustrative reasons, an algorithmic application to actual data is investigated. |
| format | Article |
| id | doaj-art-93b3b4ec588f45a18a9fb5e84a836345 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-93b3b4ec588f45a18a9fb5e84a8363452025-01-08T05:32:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031293710.1371/journal.pone.0312937Bayesian and non-bayesian analysis for stress-strength model based on progressively first failure censoring with applications.Salem A AlyamiAmal S HassanIbrahim ElbatalOlayan AlbalawiMohammed ElgarhyAhmed R El-SaeedThis article examines the estimate of ϑ = P [T < Q], using both Bayesian and non-Bayesian methods, utilizing progressively first-failure censored data. Assume that the stress (T) and strength (Q) are independent random variables that follow the Burr III distribution and the Burr XII distribution, respectively, with a common first-shape parameter. The Bayes estimator and maximum likelihood estimator of ϑ are obtained. The maximum likelihood (ML) estimator is obtained for non-Bayesian estimation, and the accompanying confidence interval is constructed using the delta approach and the asymptotic normality of ML estimators. Through the use of non-informative and gamma informative priors, the Bayes estimator of ϑ under squared error and linear exponential loss functions is produced. It is suggested that Markov chain Monte Carlo techniques be used for Bayesian estimation in order to achieve Bayes estimators and the associated credible intervals. To evaluate the effectiveness of the several estimators created, a Monte Carlo numerical analysis is also carried out. In the end, for illustrative reasons, an algorithmic application to actual data is investigated.https://doi.org/10.1371/journal.pone.0312937 |
| spellingShingle | Salem A Alyami Amal S Hassan Ibrahim Elbatal Olayan Albalawi Mohammed Elgarhy Ahmed R El-Saeed Bayesian and non-bayesian analysis for stress-strength model based on progressively first failure censoring with applications. PLoS ONE |
| title | Bayesian and non-bayesian analysis for stress-strength model based on progressively first failure censoring with applications. |
| title_full | Bayesian and non-bayesian analysis for stress-strength model based on progressively first failure censoring with applications. |
| title_fullStr | Bayesian and non-bayesian analysis for stress-strength model based on progressively first failure censoring with applications. |
| title_full_unstemmed | Bayesian and non-bayesian analysis for stress-strength model based on progressively first failure censoring with applications. |
| title_short | Bayesian and non-bayesian analysis for stress-strength model based on progressively first failure censoring with applications. |
| title_sort | bayesian and non bayesian analysis for stress strength model based on progressively first failure censoring with applications |
| url | https://doi.org/10.1371/journal.pone.0312937 |
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