A new Maxwell-Log logistic distribution and its applications for mortality rate data
In this research, we extended the Log-Logistic distribution by incorporating it into the Maxwell generalized class, resulting in the Maxwell-Log Logistic (Max-LL ) distribution. The probability density function and cumulative distribution function of the proposed distribution have been defined. The...
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| Format: | Article |
| Language: | English |
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Nigerian Society of Physical Sciences
2025-05-01
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| Series: | Journal of Nigerian Society of Physical Sciences |
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| Online Access: | https://journal.nsps.org.ng/index.php/jnsps/article/view/1976 |
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| author | Uthumporn Panitanarak Aliyu Ismail Ishaq Alfred Adewole Abiodun Hanita Daud Ahmad Abubakar Suleiman |
| author_facet | Uthumporn Panitanarak Aliyu Ismail Ishaq Alfred Adewole Abiodun Hanita Daud Ahmad Abubakar Suleiman |
| author_sort | Uthumporn Panitanarak |
| collection | DOAJ |
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In this research, we extended the Log-Logistic distribution by incorporating it into the Maxwell generalized class, resulting in the Maxwell-Log Logistic (Max-LL ) distribution. The probability density function and cumulative distribution function of the proposed distribution have been defined. The proposed distribution’s density shapes can be left or right-skewed and symmetric. The failure function of this distribution might be increasing, decreasing, or inverted bathtub forms. We discussed some essential properties of the Max-LL distribution, including moments, moment generating function, probability weighted moments, stress-strength, and order statistics. The efficiency of the model parameters has been evaluated through a simulation study utilizing a quantile function. To assess the proposed distribution’s adaptability, we applied it to two lifetime datasets: global COVID-19 mortality rates (for nations with more than 100,000 cases) and Canadian COVID-19 mortality rates. The Maxwell-Log Logistic distribution outperformed other distributions on both datasets, as evidenced by several accuracy measures. This shows that the proposed distribution is the best fit for COVID-19 mortality rate data in Canada and around the world.
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| format | Article |
| id | doaj-art-98315acff4c141f1939824d6453f693c |
| institution | Kabale University |
| issn | 2714-2817 2714-4704 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nigerian Society of Physical Sciences |
| record_format | Article |
| series | Journal of Nigerian Society of Physical Sciences |
| spelling | doaj-art-98315acff4c141f1939824d6453f693c2025-01-17T18:54:45ZengNigerian Society of Physical SciencesJournal of Nigerian Society of Physical Sciences2714-28172714-47042025-05-017210.46481/jnsps.2025.1976A new Maxwell-Log logistic distribution and its applications for mortality rate dataUthumporn Panitanarak0Aliyu Ismail Ishaq1Alfred Adewole Abiodun2Hanita Daud3Ahmad Abubakar Suleiman4Department of Biostatistics, Faculty of Public Health, Mahidol University, 10400, ThailandDepartment of Statistics, Ahmadu Bello University, Zaria, 810107, NigeriaDepartment of Statistics, University of Ilorin, Ilorin, 240003, NigeriaFundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, MalaysiaFundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Malaysia In this research, we extended the Log-Logistic distribution by incorporating it into the Maxwell generalized class, resulting in the Maxwell-Log Logistic (Max-LL ) distribution. The probability density function and cumulative distribution function of the proposed distribution have been defined. The proposed distribution’s density shapes can be left or right-skewed and symmetric. The failure function of this distribution might be increasing, decreasing, or inverted bathtub forms. We discussed some essential properties of the Max-LL distribution, including moments, moment generating function, probability weighted moments, stress-strength, and order statistics. The efficiency of the model parameters has been evaluated through a simulation study utilizing a quantile function. To assess the proposed distribution’s adaptability, we applied it to two lifetime datasets: global COVID-19 mortality rates (for nations with more than 100,000 cases) and Canadian COVID-19 mortality rates. The Maxwell-Log Logistic distribution outperformed other distributions on both datasets, as evidenced by several accuracy measures. This shows that the proposed distribution is the best fit for COVID-19 mortality rate data in Canada and around the world. https://journal.nsps.org.ng/index.php/jnsps/article/view/1976Log-Logistic distribution Maxwell generalized familyMaxwell-Log Logistic distributionOrder statisticsCOVID-19 |
| spellingShingle | Uthumporn Panitanarak Aliyu Ismail Ishaq Alfred Adewole Abiodun Hanita Daud Ahmad Abubakar Suleiman A new Maxwell-Log logistic distribution and its applications for mortality rate data Journal of Nigerian Society of Physical Sciences Log-Logistic distribution Maxwell generalized family Maxwell-Log Logistic distribution Order statistics COVID-19 |
| title | A new Maxwell-Log logistic distribution and its applications for mortality rate data |
| title_full | A new Maxwell-Log logistic distribution and its applications for mortality rate data |
| title_fullStr | A new Maxwell-Log logistic distribution and its applications for mortality rate data |
| title_full_unstemmed | A new Maxwell-Log logistic distribution and its applications for mortality rate data |
| title_short | A new Maxwell-Log logistic distribution and its applications for mortality rate data |
| title_sort | new maxwell log logistic distribution and its applications for mortality rate data |
| topic | Log-Logistic distribution Maxwell generalized family Maxwell-Log Logistic distribution Order statistics COVID-19 |
| url | https://journal.nsps.org.ng/index.php/jnsps/article/view/1976 |
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