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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Bibliographic Details
Published in:PloS one 2024-12, Vol.19 (12), p.e0312937
Main Authors: Alyami, Salem A, Hassan, Amal S, Elbatal, Ibrahim, Albalawi, Olayan, Elgarhy, Mohammed, El-Saeed, Ahmed R
Format: Article
Language:English
Subjects:
Algorithms
Bayes Theorem
Bayesian analysis
Censorship
Confidence intervals
Error analysis
Estimates
Humans
Independent variables
Likelihood Functions
Markov Chains
Maximum likelihood estimators
Models, Statistical
Monte Carlo Method
Normality
Numerical analysis
Parameter estimation
Random variables
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0312937