The Neutrosophic Lognormal Model in Lifetime Data Analysis: Properties and Applications
The lognormal distribution is more extensively used in the domain of reliability analysis for modeling the life-failure patterns of numerous devices. In this paper, a generic form of the lognormal distribution is presented that can be applied to model many engineering problems involving indeterminac...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Function Spaces |
| Online Access: | http://dx.doi.org/10.1155/2021/6337759 |
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| _version_ | 1850234283908661248 |
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| author | Sultan Salem Zahid Khan Hamdi Ayed Ameni Brahmia Adnan Amin |
| author_facet | Sultan Salem Zahid Khan Hamdi Ayed Ameni Brahmia Adnan Amin |
| author_sort | Sultan Salem |
| collection | DOAJ |
| description | The lognormal distribution is more extensively used in the domain of reliability analysis for modeling the life-failure patterns of numerous devices. In this paper, a generic form of the lognormal distribution is presented that can be applied to model many engineering problems involving indeterminacies in reliability studies. The suggested distribution is especially effective for modeling data that are roughly symmetric or skewed to the right. In this paper, the key mathematical properties of the proposed neutrosophic lognormal distribution (NLD) have been derived. Throughout the study, detailed examples from life-test data are used to confirm the mathematical development of the proposed neutrosophic model. The core ideas of the reliability terms, including the neutrosophic mean time failure, neutrosophic hazard rate, neutrosophic cumulative failure rate, and neutrosophic reliability function, are addressed with examples. In addition, the estimation of two typical parameters of the NLD by mean of maximum likelihood (ML) approach under the neutrosophic environment is described. A simulation experiment is run to determine the performance of the estimated parameters. Simulated findings suggest that ML estimators effectively estimate the unknown parameters with a large sample size. Finally, a real dataset on ball bearings failure times has been considered an application of the proposed model. |
| format | Article |
| id | doaj-art-15cb5f7e58ef4cfb9f089f71b9b6cec0 |
| institution | OA Journals |
| issn | 2314-8888 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Function Spaces |
| spelling | doaj-art-15cb5f7e58ef4cfb9f089f71b9b6cec02025-08-20T02:02:40ZengWileyJournal of Function Spaces2314-88882021-01-01202110.1155/2021/6337759The Neutrosophic Lognormal Model in Lifetime Data Analysis: Properties and ApplicationsSultan Salem0Zahid Khan1Hamdi Ayed2Ameni Brahmia3Adnan Amin4Department of EconomicsDepartment of Mathematics and StatisticsDepartment of Civil EngineeringDepartment of ChemistryDepartment of StatisticsThe lognormal distribution is more extensively used in the domain of reliability analysis for modeling the life-failure patterns of numerous devices. In this paper, a generic form of the lognormal distribution is presented that can be applied to model many engineering problems involving indeterminacies in reliability studies. The suggested distribution is especially effective for modeling data that are roughly symmetric or skewed to the right. In this paper, the key mathematical properties of the proposed neutrosophic lognormal distribution (NLD) have been derived. Throughout the study, detailed examples from life-test data are used to confirm the mathematical development of the proposed neutrosophic model. The core ideas of the reliability terms, including the neutrosophic mean time failure, neutrosophic hazard rate, neutrosophic cumulative failure rate, and neutrosophic reliability function, are addressed with examples. In addition, the estimation of two typical parameters of the NLD by mean of maximum likelihood (ML) approach under the neutrosophic environment is described. A simulation experiment is run to determine the performance of the estimated parameters. Simulated findings suggest that ML estimators effectively estimate the unknown parameters with a large sample size. Finally, a real dataset on ball bearings failure times has been considered an application of the proposed model.http://dx.doi.org/10.1155/2021/6337759 |
| spellingShingle | Sultan Salem Zahid Khan Hamdi Ayed Ameni Brahmia Adnan Amin The Neutrosophic Lognormal Model in Lifetime Data Analysis: Properties and Applications Journal of Function Spaces |
| title | The Neutrosophic Lognormal Model in Lifetime Data Analysis: Properties and Applications |
| title_full | The Neutrosophic Lognormal Model in Lifetime Data Analysis: Properties and Applications |
| title_fullStr | The Neutrosophic Lognormal Model in Lifetime Data Analysis: Properties and Applications |
| title_full_unstemmed | The Neutrosophic Lognormal Model in Lifetime Data Analysis: Properties and Applications |
| title_short | The Neutrosophic Lognormal Model in Lifetime Data Analysis: Properties and Applications |
| title_sort | neutrosophic lognormal model in lifetime data analysis properties and applications |
| url | http://dx.doi.org/10.1155/2021/6337759 |
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