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Utilizing Bayesian inference in accelerated testing models under constant stress via ordered ranked set sampling and
Atef F Hashem1,2, Naif Alotaibi3, Salem A Alyami3
1Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia. affaragalla@imamu.edu.sa.
Scientific Reports
|June 22, 2024
Summary
This study explores ordered ranked set sampling (ORSSA) for estimating parameters in constant-stress partially accelerated life-testing (CSPALTE) using Bayesian methods. ORSSA demonstrates improved reliability analysis efficiency compared to simple random sampling under hybrid censoring.
Area of Science:
- Reliability Engineering
- Statistical Inference
- Accelerated Life Testing
Background:
- Constant-stress partially accelerated life-testing (CSPALTE) is crucial for product reliability assessment.
- Traditional sampling methods may not be optimal for parameter estimation in CSPALTE.
- Bayesian estimation offers a robust framework for analyzing life-testing data.
Purpose of the Study:
- To investigate the application and efficacy of ordered ranked set sampling (ORSSA) in CSPALTE.
- To estimate parameters of the half-logistic distribution under CSPALTE using Bayesian methods.
- To compare the performance of ORSSA with simple random sampling (SRS) under hybrid censoring.
Main Methods:
- Utilized Bayesian estimation with both symmetric and asymmetric loss functions.
- Employed ordered ranked set sampling (ORSSA) and simple random sampling (SRS).
- Incorporated type-I hybrid censoring and a half-logistic life distribution model.
- Conducted simulation studies with numerical calculations for performance evaluation.
Main Results:
- Bayesian estimation using ORSSA provided more efficient parameter estimates compared to SRS.
- The study validated theoretical findings using real-world data sets.
- Simulation results demonstrated the superiority of ORSSA in various scenarios.
Conclusions:
- Ordered ranked set sampling (ORSSA) enhances the precision of Bayesian parameter estimation in CSPALTE.
- The findings contribute to improved reliability analysis methodologies for products under stress.
- Bayesian approaches combined with ORSSA offer a powerful tool for life-testing data analysis.
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