A new statistical approach with simulation study: Its implementations in management sciences and reliability
The analysis of practical phenomena is fundamentally reliant on probability distributions. This awareness has inspired researchers to develop new statistical models, resulting in a range of methodologies. Many of these methodologies are typically established with new parameters. Unfortunately, the a...
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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/S1110016825001401 |
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| author | Zhidong Liang |
| author_facet | Zhidong Liang |
| author_sort | Zhidong Liang |
| collection | DOAJ |
| description | The analysis of practical phenomena is fundamentally reliant on probability distributions. This awareness has inspired researchers to develop new statistical models, resulting in a range of methodologies. Many of these methodologies are typically established with new parameters. Unfortunately, the addition of extra parameters can occasionally lead to complications concerning re-parameterization. Within this specific research domain, we propose a new statistical methodology intended to augment the distributional flexibility of probability models while avoiding the need for new parameters. This methodology, which merges the cosine function with the weighted T-X strategy, is designated as the cosine weighted-G (CW-G) family. We focus on the cosine weighted-Weibull (CW-Weibull) distribution, obtained through the CW-G method. Certain fundamental distributional functions pertaining to the CW-Weibull distribution are outlined, accompanied by visual depictions. We derive the quartile-based properties and formulates the maximum likelihood estimators. Additionally, a simulation study is performed to validate the theoretical findings. The relevance of the CW-Weibull distribution is affirmed through the scrutiny of two real-world data sets sourced from management sciences and reliability sectors. Our findings, derived from specific evaluation tests, indicate that the CW-Weibull distribution achieves optimal performance in the analysis of these data sets. |
| format | Article |
| id | doaj-art-a7769fe3047f48ee8487330db029dd57 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-a7769fe3047f48ee8487330db029dd572025-02-11T04:33:36ZengElsevierAlexandria Engineering Journal1110-01682025-04-01119531544A new statistical approach with simulation study: Its implementations in management sciences and reliabilityZhidong Liang0Faculty of Economics and Management, Qilu Normal University, Jinan city, 250100, Shandong Province, ChinaThe analysis of practical phenomena is fundamentally reliant on probability distributions. This awareness has inspired researchers to develop new statistical models, resulting in a range of methodologies. Many of these methodologies are typically established with new parameters. Unfortunately, the addition of extra parameters can occasionally lead to complications concerning re-parameterization. Within this specific research domain, we propose a new statistical methodology intended to augment the distributional flexibility of probability models while avoiding the need for new parameters. This methodology, which merges the cosine function with the weighted T-X strategy, is designated as the cosine weighted-G (CW-G) family. We focus on the cosine weighted-Weibull (CW-Weibull) distribution, obtained through the CW-G method. Certain fundamental distributional functions pertaining to the CW-Weibull distribution are outlined, accompanied by visual depictions. We derive the quartile-based properties and formulates the maximum likelihood estimators. Additionally, a simulation study is performed to validate the theoretical findings. The relevance of the CW-Weibull distribution is affirmed through the scrutiny of two real-world data sets sourced from management sciences and reliability sectors. Our findings, derived from specific evaluation tests, indicate that the CW-Weibull distribution achieves optimal performance in the analysis of these data sets.http://www.sciencedirect.com/science/article/pii/S1110016825001401Weibull distributionTrigonometric functionCosine functionEmpirical investigationManagement and reliability dataStatistical analysis |
| spellingShingle | Zhidong Liang A new statistical approach with simulation study: Its implementations in management sciences and reliability Alexandria Engineering Journal Weibull distribution Trigonometric function Cosine function Empirical investigation Management and reliability data Statistical analysis |
| title | A new statistical approach with simulation study: Its implementations in management sciences and reliability |
| title_full | A new statistical approach with simulation study: Its implementations in management sciences and reliability |
| title_fullStr | A new statistical approach with simulation study: Its implementations in management sciences and reliability |
| title_full_unstemmed | A new statistical approach with simulation study: Its implementations in management sciences and reliability |
| title_short | A new statistical approach with simulation study: Its implementations in management sciences and reliability |
| title_sort | new statistical approach with simulation study its implementations in management sciences and reliability |
| topic | Weibull distribution Trigonometric function Cosine function Empirical investigation Management and reliability data Statistical analysis |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825001401 |
| work_keys_str_mv | AT zhidongliang anewstatisticalapproachwithsimulationstudyitsimplementationsinmanagementsciencesandreliability AT zhidongliang newstatisticalapproachwithsimulationstudyitsimplementationsinmanagementsciencesandreliability |