Robust estimation of the three parameter Weibull distribution for addressing outliers in reliability analysis
Abstract Accurate estimation techniques are crucial in statistical modeling and reliability analysis, which have significant applications across various industries. The three-parameter Weibull distribution is a widely used tool in this context, but traditional estimation methods often struggle with...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96043-1 |
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| author | Muhammad Aslam Mohd Safari Nurulkamal Masseran Muhammad Hilmi Abdul Majid Razik Ridzuan Mohd Tajuddin |
| author_facet | Muhammad Aslam Mohd Safari Nurulkamal Masseran Muhammad Hilmi Abdul Majid Razik Ridzuan Mohd Tajuddin |
| author_sort | Muhammad Aslam Mohd Safari |
| collection | DOAJ |
| description | Abstract Accurate estimation techniques are crucial in statistical modeling and reliability analysis, which have significant applications across various industries. The three-parameter Weibull distribution is a widely used tool in this context, but traditional estimation methods often struggle with outliers, resulting in unreliable parameter estimates. To address this issue, our study introduces a robust estimation technique for the three-parameter Weibull distribution, leveraging the probability integral transform and specifically employing the Weibull survival function for the transformation, with a focus on complete data. This method is designed to enhance robustness while maintaining computational simplicity, making it easy to implement. Through extensive simulation studies, we demonstrate the effectiveness and resilience of our proposed estimator in the presence of outliers. The findings indicate that this new technique significantly improves the accuracy of Weibull parameter estimates, thereby expanding the toolkit available to researchers and practitioners in reliability data analysis. Furthermore, we apply the proposed method to real-world reliability datasets, confirming its practical utility and effectiveness in overcoming the limitations of existing estimation methodologies in the presence of outliers. |
| format | Article |
| id | doaj-art-29bc2ab58d5646efbc98ce6a9b0416b4 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-29bc2ab58d5646efbc98ce6a9b0416b42025-08-20T02:08:08ZengNature PortfolioScientific Reports2045-23222025-04-0115111410.1038/s41598-025-96043-1Robust estimation of the three parameter Weibull distribution for addressing outliers in reliability analysisMuhammad Aslam Mohd Safari0Nurulkamal Masseran1Muhammad Hilmi Abdul Majid2Razik Ridzuan Mohd Tajuddin3Department of Mathematics and Statistics, Faculty of Science, Universiti Putra MalaysiaDepartment of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan MalaysiaFaculty of Computer Science and Mathematics, Universiti Malaysia TerengganuDepartment of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan MalaysiaAbstract Accurate estimation techniques are crucial in statistical modeling and reliability analysis, which have significant applications across various industries. The three-parameter Weibull distribution is a widely used tool in this context, but traditional estimation methods often struggle with outliers, resulting in unreliable parameter estimates. To address this issue, our study introduces a robust estimation technique for the three-parameter Weibull distribution, leveraging the probability integral transform and specifically employing the Weibull survival function for the transformation, with a focus on complete data. This method is designed to enhance robustness while maintaining computational simplicity, making it easy to implement. Through extensive simulation studies, we demonstrate the effectiveness and resilience of our proposed estimator in the presence of outliers. The findings indicate that this new technique significantly improves the accuracy of Weibull parameter estimates, thereby expanding the toolkit available to researchers and practitioners in reliability data analysis. Furthermore, we apply the proposed method to real-world reliability datasets, confirming its practical utility and effectiveness in overcoming the limitations of existing estimation methodologies in the presence of outliers.https://doi.org/10.1038/s41598-025-96043-1Probability integral transformRobust estimationWeibull distributionMonte Carlo simulationOutliers |
| spellingShingle | Muhammad Aslam Mohd Safari Nurulkamal Masseran Muhammad Hilmi Abdul Majid Razik Ridzuan Mohd Tajuddin Robust estimation of the three parameter Weibull distribution for addressing outliers in reliability analysis Scientific Reports Probability integral transform Robust estimation Weibull distribution Monte Carlo simulation Outliers |
| title | Robust estimation of the three parameter Weibull distribution for addressing outliers in reliability analysis |
| title_full | Robust estimation of the three parameter Weibull distribution for addressing outliers in reliability analysis |
| title_fullStr | Robust estimation of the three parameter Weibull distribution for addressing outliers in reliability analysis |
| title_full_unstemmed | Robust estimation of the three parameter Weibull distribution for addressing outliers in reliability analysis |
| title_short | Robust estimation of the three parameter Weibull distribution for addressing outliers in reliability analysis |
| title_sort | robust estimation of the three parameter weibull distribution for addressing outliers in reliability analysis |
| topic | Probability integral transform Robust estimation Weibull distribution Monte Carlo simulation Outliers |
| url | https://doi.org/10.1038/s41598-025-96043-1 |
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