New Statistical Approaches for Modeling the COVID-19 Data Set: A Case Study in the Medical Sector
Statistical distributions have great applicability for modeling data in almost every applied sector. Among the available classical distributions, the inverse Weibull distribution has received considerable attention. In the practice of distribution theory, numerous methods have been studied and sugge...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/1325825 |
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| _version_ | 1832549018511605760 |
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| author | Mohammed M. A. Almazah Kalim Ullah Eslam Hussam Md. Moyazzem Hossain Ramy Aldallal Fathy H. Riad |
| author_facet | Mohammed M. A. Almazah Kalim Ullah Eslam Hussam Md. Moyazzem Hossain Ramy Aldallal Fathy H. Riad |
| author_sort | Mohammed M. A. Almazah |
| collection | DOAJ |
| description | Statistical distributions have great applicability for modeling data in almost every applied sector. Among the available classical distributions, the inverse Weibull distribution has received considerable attention. In the practice of distribution theory, numerous methods have been studied and suggested/introduced to increase the flexibility level of the traditional probability distributions. In this paper, we implement different distribution methods to obtain five new different versions of the inverse Weibull model. The new modifications of the inverse Weibull model are called the logarithm transformed-inverse Weibull, a flexible reduced logarithmic-inverse Weibull, the weighted TX-inverse Weibull, a new generalized-inverse Weibull, and the alpha power transformed extended-inverse Weibull distributions. To illustrate the flexibility and applicability of the new modifications of the inverse Weibull model, a biomedical data set is analyzed. The data set consists of 108 observations and represents the mortality rate of the COVID-19-infected patients. The practical application shows that the new generalized-inverse Weibull is the best modification of the inverse Weibull distribution. |
| format | Article |
| id | doaj-art-38b5416820fc49b2be05ef49727449e4 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-38b5416820fc49b2be05ef49727449e42025-02-03T06:12:26ZengWileyComplexity1099-05262022-01-01202210.1155/2022/1325825New Statistical Approaches for Modeling the COVID-19 Data Set: A Case Study in the Medical SectorMohammed M. A. Almazah0Kalim Ullah1Eslam Hussam2Md. Moyazzem Hossain3Ramy Aldallal4Fathy H. Riad5Department of MathematicsFoundation University Medical CollegeDepartment of MathematicsDepartment of StatisticsDepartment of AccountingMathematics DepartmentStatistical distributions have great applicability for modeling data in almost every applied sector. Among the available classical distributions, the inverse Weibull distribution has received considerable attention. In the practice of distribution theory, numerous methods have been studied and suggested/introduced to increase the flexibility level of the traditional probability distributions. In this paper, we implement different distribution methods to obtain five new different versions of the inverse Weibull model. The new modifications of the inverse Weibull model are called the logarithm transformed-inverse Weibull, a flexible reduced logarithmic-inverse Weibull, the weighted TX-inverse Weibull, a new generalized-inverse Weibull, and the alpha power transformed extended-inverse Weibull distributions. To illustrate the flexibility and applicability of the new modifications of the inverse Weibull model, a biomedical data set is analyzed. The data set consists of 108 observations and represents the mortality rate of the COVID-19-infected patients. The practical application shows that the new generalized-inverse Weibull is the best modification of the inverse Weibull distribution.http://dx.doi.org/10.1155/2022/1325825 |
| spellingShingle | Mohammed M. A. Almazah Kalim Ullah Eslam Hussam Md. Moyazzem Hossain Ramy Aldallal Fathy H. Riad New Statistical Approaches for Modeling the COVID-19 Data Set: A Case Study in the Medical Sector Complexity |
| title | New Statistical Approaches for Modeling the COVID-19 Data Set: A Case Study in the Medical Sector |
| title_full | New Statistical Approaches for Modeling the COVID-19 Data Set: A Case Study in the Medical Sector |
| title_fullStr | New Statistical Approaches for Modeling the COVID-19 Data Set: A Case Study in the Medical Sector |
| title_full_unstemmed | New Statistical Approaches for Modeling the COVID-19 Data Set: A Case Study in the Medical Sector |
| title_short | New Statistical Approaches for Modeling the COVID-19 Data Set: A Case Study in the Medical Sector |
| title_sort | new statistical approaches for modeling the covid 19 data set a case study in the medical sector |
| url | http://dx.doi.org/10.1155/2022/1325825 |
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