Reliability inference for multi-component stress-strength systems with heterogeneous Lomax-distributed components under progressive censoring
Abstract In this paper, the m-component stress-strength parameter is considered under progressive first failure (PFF) censoring scheme, assuming the stress and strength variables follow the Lomax distribution. To achieve this, various classical and Bayesian estimation methods are explored under two...
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| Format: | Article |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-00846-1 |
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| author | Akram Kohansal Hassan S. Bakouch Reza Pakyari |
| author_facet | Akram Kohansal Hassan S. Bakouch Reza Pakyari |
| author_sort | Akram Kohansal |
| collection | DOAJ |
| description | Abstract In this paper, the m-component stress-strength parameter is considered under progressive first failure (PFF) censoring scheme, assuming the stress and strength variables follow the Lomax distribution. To achieve this, various classical and Bayesian estimation methods are explored under two scenarios. In the first scenario, where the common parameters of the stress and strength variables are unknown, maximum likelihood estimation (MLE), Bayesian estimation using the Markov Chain Monte Carlo (MCMC) method, asymptotic confidence intervals, and highest posterior density (HPD) credible intervals are derived. In the second scenario, where the common parameters of the stress and strength variables are known, MLE, Bayesian estimation using the MCMC method and Lindley’s approximation, the uniformly minimum variance unbiased estimator (UMVUE), as well as the asymptotic confidence intervals and the HPD credible intervals, are derived. A Monte Carlo simulation study is conducted to evaluate and compare the performance of the proposed methods. Additionally, two real datasets are analyzed for illustrative purposes. |
| format | Article |
| id | doaj-art-705137ddc1b0457db5b358f04450a96e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-705137ddc1b0457db5b358f04450a96e2025-08-20T02:32:01ZengNature PortfolioScientific Reports2045-23222025-05-0115111710.1038/s41598-025-00846-1Reliability inference for multi-component stress-strength systems with heterogeneous Lomax-distributed components under progressive censoringAkram Kohansal0Hassan S. Bakouch1Reza Pakyari2Department of Statistics, Imam Khomeini International UniversityDepartment of Mathematics, Faculty of Science, Tanta UniversityStatistics Program, Department of Mathematics and Statistics, College of Arts and Sciences, Qatar UniversityAbstract In this paper, the m-component stress-strength parameter is considered under progressive first failure (PFF) censoring scheme, assuming the stress and strength variables follow the Lomax distribution. To achieve this, various classical and Bayesian estimation methods are explored under two scenarios. In the first scenario, where the common parameters of the stress and strength variables are unknown, maximum likelihood estimation (MLE), Bayesian estimation using the Markov Chain Monte Carlo (MCMC) method, asymptotic confidence intervals, and highest posterior density (HPD) credible intervals are derived. In the second scenario, where the common parameters of the stress and strength variables are known, MLE, Bayesian estimation using the MCMC method and Lindley’s approximation, the uniformly minimum variance unbiased estimator (UMVUE), as well as the asymptotic confidence intervals and the HPD credible intervals, are derived. A Monte Carlo simulation study is conducted to evaluate and compare the performance of the proposed methods. Additionally, two real datasets are analyzed for illustrative purposes.https://doi.org/10.1038/s41598-025-00846-1Multi-component stress-strength parameterProgressive first failure censoringLomax distributionMCMC methodSimulationLindley’s approximation |
| spellingShingle | Akram Kohansal Hassan S. Bakouch Reza Pakyari Reliability inference for multi-component stress-strength systems with heterogeneous Lomax-distributed components under progressive censoring Scientific Reports Multi-component stress-strength parameter Progressive first failure censoring Lomax distribution MCMC method Simulation Lindley’s approximation |
| title | Reliability inference for multi-component stress-strength systems with heterogeneous Lomax-distributed components under progressive censoring |
| title_full | Reliability inference for multi-component stress-strength systems with heterogeneous Lomax-distributed components under progressive censoring |
| title_fullStr | Reliability inference for multi-component stress-strength systems with heterogeneous Lomax-distributed components under progressive censoring |
| title_full_unstemmed | Reliability inference for multi-component stress-strength systems with heterogeneous Lomax-distributed components under progressive censoring |
| title_short | Reliability inference for multi-component stress-strength systems with heterogeneous Lomax-distributed components under progressive censoring |
| title_sort | reliability inference for multi component stress strength systems with heterogeneous lomax distributed components under progressive censoring |
| topic | Multi-component stress-strength parameter Progressive first failure censoring Lomax distribution MCMC method Simulation Lindley’s approximation |
| url | https://doi.org/10.1038/s41598-025-00846-1 |
| work_keys_str_mv | AT akramkohansal reliabilityinferenceformulticomponentstressstrengthsystemswithheterogeneouslomaxdistributedcomponentsunderprogressivecensoring AT hassansbakouch reliabilityinferenceformulticomponentstressstrengthsystemswithheterogeneouslomaxdistributedcomponentsunderprogressivecensoring AT rezapakyari reliabilityinferenceformulticomponentstressstrengthsystemswithheterogeneouslomaxdistributedcomponentsunderprogressivecensoring |