A new trigonometric-inspired probability distribution: A simulation study and applications in reliability and hydrology
The importance of statistical distributions in accurately representing real-world scenarios and aiding in educated decision-making is well recognized. Nonetheless, it is also true that the limitations of these distributions can hinder optimal fitting in certain situations. This awareness has led res...
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Elsevier
2025-02-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824014510 |
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| author | Xiang Tu Jiangwei Kong Qing Fu Sheng Chang Kunfeng Zhang Tmader Alballa Haifa Alqahtani Hamiden Abd El-Wahed Khalifa |
| author_facet | Xiang Tu Jiangwei Kong Qing Fu Sheng Chang Kunfeng Zhang Tmader Alballa Haifa Alqahtani Hamiden Abd El-Wahed Khalifa |
| author_sort | Xiang Tu |
| collection | DOAJ |
| description | The importance of statistical distributions in accurately representing real-world scenarios and aiding in educated decision-making is well recognized. Nonetheless, it is also true that the limitations of these distributions can hinder optimal fitting in certain situations. This awareness has led researchers to seek out improved and more optimal probability distributions. Based on factual motivation, this paper introduces a new probability distribution called the weighted sine generalized inverse Weibull (WSGI-Weibull) distribution. This model emerges from the amalgamation of the generalized inverse Weibull distribution and a sine-inspired probabilistic framework. Certain statistical properties, particularly those based on quantiles, of the newly introduced WSGI-Weibull distribution have been derived. An established estimation method is applied to calculate the point estimators of the WSGI-Weibull distribution, and subsequently, a simulation study is conducted. To highlight the benefits of the WSGI-Weibull distribution, two data sets sourced from the reliability and hydrology sectors are analyzed. The empirical fitting of the WSGI-Weibull distribution is evaluated against specific adversarial distributions, utilizing the two data sets as a basis for comparison. Utilizing specific evaluation tools, it has been noted that the WSGI-Weibull distribution delivers the best and most optimal fit for the reliability and hydrological data sets. |
| format | Article |
| id | doaj-art-bc5ad7c258c845cbaa5a4dc4c0e95496 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-bc5ad7c258c845cbaa5a4dc4c0e954962025-02-07T04:47:04ZengElsevierAlexandria Engineering Journal1110-01682025-02-01113181194A new trigonometric-inspired probability distribution: A simulation study and applications in reliability and hydrologyXiang Tu0Jiangwei Kong1Qing Fu2Sheng Chang3Kunfeng Zhang4Tmader Alballa5Haifa Alqahtani6Hamiden Abd El-Wahed Khalifa7State Key Laboratory of Environmental Criteria and Risk Assessment, State Environmental Protection Key Laboratory of Drinking Water Source Protection, Research Centre of Lake Environment, National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, 100012, Beijing, ChinaSchool of Architecture, Tianjin University, 300072, Tianjin, China; Corresponding author.State Key Laboratory of Environmental Criteria and Risk Assessment, State Environmental Protection Key Laboratory of Drinking Water Source Protection, Research Centre of Lake Environment, National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, 100012, Beijing, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, State Environmental Protection Key Laboratory of Drinking Water Source Protection, Research Centre of Lake Environment, National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, 100012, Beijing, ChinaMIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, Heilongjiang, ChinaDepartment of Mathematics, College of Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Statistics and Business Analytics, United Arab Emirates University, Al Ain 15551, Abu Dhabi, United Arab EmiratesDepartment of Mathematics, College of Science, Qassim University, Buraydah 51452, Saudi ArabiaThe importance of statistical distributions in accurately representing real-world scenarios and aiding in educated decision-making is well recognized. Nonetheless, it is also true that the limitations of these distributions can hinder optimal fitting in certain situations. This awareness has led researchers to seek out improved and more optimal probability distributions. Based on factual motivation, this paper introduces a new probability distribution called the weighted sine generalized inverse Weibull (WSGI-Weibull) distribution. This model emerges from the amalgamation of the generalized inverse Weibull distribution and a sine-inspired probabilistic framework. Certain statistical properties, particularly those based on quantiles, of the newly introduced WSGI-Weibull distribution have been derived. An established estimation method is applied to calculate the point estimators of the WSGI-Weibull distribution, and subsequently, a simulation study is conducted. To highlight the benefits of the WSGI-Weibull distribution, two data sets sourced from the reliability and hydrology sectors are analyzed. The empirical fitting of the WSGI-Weibull distribution is evaluated against specific adversarial distributions, utilizing the two data sets as a basis for comparison. Utilizing specific evaluation tools, it has been noted that the WSGI-Weibull distribution delivers the best and most optimal fit for the reliability and hydrological data sets.http://www.sciencedirect.com/science/article/pii/S1110016824014510Inverse Weibull distributionSine functionQuartile-based propertiesEstimation and simulationReliabilityHydrology |
| spellingShingle | Xiang Tu Jiangwei Kong Qing Fu Sheng Chang Kunfeng Zhang Tmader Alballa Haifa Alqahtani Hamiden Abd El-Wahed Khalifa A new trigonometric-inspired probability distribution: A simulation study and applications in reliability and hydrology Alexandria Engineering Journal Inverse Weibull distribution Sine function Quartile-based properties Estimation and simulation Reliability Hydrology |
| title | A new trigonometric-inspired probability distribution: A simulation study and applications in reliability and hydrology |
| title_full | A new trigonometric-inspired probability distribution: A simulation study and applications in reliability and hydrology |
| title_fullStr | A new trigonometric-inspired probability distribution: A simulation study and applications in reliability and hydrology |
| title_full_unstemmed | A new trigonometric-inspired probability distribution: A simulation study and applications in reliability and hydrology |
| title_short | A new trigonometric-inspired probability distribution: A simulation study and applications in reliability and hydrology |
| title_sort | new trigonometric inspired probability distribution a simulation study and applications in reliability and hydrology |
| topic | Inverse Weibull distribution Sine function Quartile-based properties Estimation and simulation Reliability Hydrology |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824014510 |
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