A new statistical model with optimal fitting performance: Its assessments in management sciences and reliability
The study of real-world phenomena fundamentally hinges on probability distributions. This understanding has inspired researchers to design new statistical models, which has resulted in a variety of methodologies. Often, these methodologies are developed with new parameters. Unfortunately, the introd...
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Elsevier
2025-04-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825001218 |
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| author | Zhidong Liang |
| author_facet | Zhidong Liang |
| author_sort | Zhidong Liang |
| collection | DOAJ |
| description | The study of real-world phenomena fundamentally hinges on probability distributions. This understanding has inspired researchers to design new statistical models, which has resulted in a variety of methodologies. Often, these methodologies are developed with new parameters. Unfortunately, the introduction of additional parameters can sometimes create difficulties related to re-parameterization. In the context of this particular research area, we introduce a groundbreaking statistical methodology designed to enhance the distributional flexibility of probability models without the addition of new parameters. The methodology we propose, which combines the sine function with the weighted T-X strategy, is referred to as the sine weighted-G (SW-G) family. The sine weighted-Weibull (SW-Weibull) distribution is examined through the SW-G method. Essential distributional functions for the SW-Weibull distribution are presented, along with corresponding visual representations. Additionally, properties based on quartiles are explored, and the derivation of maximum likelihood estimators is presented. A simulation study is conducted to enhance the understanding of the distribution. Ultimately, the relevance of the SW-Weibull distribution is confirmed by examining two real-world data sets from the management sciences and reliability sectors. Our findings, based on particular evaluation tests, indicate that the SW-Weibull distribution provides optimal performance when analyzing the aforementioned data sets. |
| format | Article |
| id | doaj-art-7a3a935bb2064c30b52126a9d5555647 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-7a3a935bb2064c30b52126a9d55556472025-02-11T04:33:36ZengElsevierAlexandria Engineering Journal1110-01682025-04-01119545557A new statistical model with optimal fitting performance: Its assessments in management sciences and reliabilityZhidong Liang0Faculty of Economics and Management, Qilu Normal University, Jinan city, 250100, Shandong Province, ChinaThe study of real-world phenomena fundamentally hinges on probability distributions. This understanding has inspired researchers to design new statistical models, which has resulted in a variety of methodologies. Often, these methodologies are developed with new parameters. Unfortunately, the introduction of additional parameters can sometimes create difficulties related to re-parameterization. In the context of this particular research area, we introduce a groundbreaking statistical methodology designed to enhance the distributional flexibility of probability models without the addition of new parameters. The methodology we propose, which combines the sine function with the weighted T-X strategy, is referred to as the sine weighted-G (SW-G) family. The sine weighted-Weibull (SW-Weibull) distribution is examined through the SW-G method. Essential distributional functions for the SW-Weibull distribution are presented, along with corresponding visual representations. Additionally, properties based on quartiles are explored, and the derivation of maximum likelihood estimators is presented. A simulation study is conducted to enhance the understanding of the distribution. Ultimately, the relevance of the SW-Weibull distribution is confirmed by examining two real-world data sets from the management sciences and reliability sectors. Our findings, based on particular evaluation tests, indicate that the SW-Weibull distribution provides optimal performance when analyzing the aforementioned data sets.http://www.sciencedirect.com/science/article/pii/S1110016825001218Weibull distributionSine functionQuartile functionManagement sciencesReliability sectorStatistical modeling |
| spellingShingle | Zhidong Liang A new statistical model with optimal fitting performance: Its assessments in management sciences and reliability Alexandria Engineering Journal Weibull distribution Sine function Quartile function Management sciences Reliability sector Statistical modeling |
| title | A new statistical model with optimal fitting performance: Its assessments in management sciences and reliability |
| title_full | A new statistical model with optimal fitting performance: Its assessments in management sciences and reliability |
| title_fullStr | A new statistical model with optimal fitting performance: Its assessments in management sciences and reliability |
| title_full_unstemmed | A new statistical model with optimal fitting performance: Its assessments in management sciences and reliability |
| title_short | A new statistical model with optimal fitting performance: Its assessments in management sciences and reliability |
| title_sort | new statistical model with optimal fitting performance its assessments in management sciences and reliability |
| topic | Weibull distribution Sine function Quartile function Management sciences Reliability sector Statistical modeling |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825001218 |
| work_keys_str_mv | AT zhidongliang anewstatisticalmodelwithoptimalfittingperformanceitsassessmentsinmanagementsciencesandreliability AT zhidongliang newstatisticalmodelwithoptimalfittingperformanceitsassessmentsinmanagementsciencesandreliability |