A new generalization of Gull Alpha Power Family of distributions with application to modeling COVID-19 mortality rates
This paper proposes a new generalization of the Gull Alpha Power Family of distribution, namely the exponentiated generalized gull alpha power family of distribution abbreviated as (EGGAPF) with two additional parameters. This proposed family of distributions has some well known sub-models. Some of...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-05-01
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| Series: | Results in Physics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S221137972200119X |
| _version_ | 1819061106785648640 |
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| author | Mutua Kilai Gichuhi A. Waititu Wanjoya A. Kibira Huda M. Alshanbari M. El-Morshedy |
| author_facet | Mutua Kilai Gichuhi A. Waititu Wanjoya A. Kibira Huda M. Alshanbari M. El-Morshedy |
| author_sort | Mutua Kilai |
| collection | DOAJ |
| description | This paper proposes a new generalization of the Gull Alpha Power Family of distribution, namely the exponentiated generalized gull alpha power family of distribution abbreviated as (EGGAPF) with two additional parameters. This proposed family of distributions has some well known sub-models. Some of the basic properties of the distribution like the hazard function, survival function, order statistics, quantile function, moment generating function are investigated. In order to estimate the parameters of the model the method of maximum likelihood estimation is used. To assess the performance of the MLE estimates a simulation study was performed. It is observed that with increase in sample size, the average bias, and the RMSE decrease. A distribution from this family is fitted to two real data sets and compared to its sub-models. It can be concluded that the proposed distribution outperforms its sub-models. |
| first_indexed | 2024-12-21T14:37:37Z |
| format | Article |
| id | doaj.art-861e4448139d4450b0cff63f19a2b99e |
| institution | Directory Open Access Journal |
| issn | 2211-3797 |
| language | English |
| last_indexed | 2024-12-21T14:37:37Z |
| publishDate | 2022-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Physics |
| spelling | doaj.art-861e4448139d4450b0cff63f19a2b99e2022-12-21T19:00:18ZengElsevierResults in Physics2211-37972022-05-0136105339A new generalization of Gull Alpha Power Family of distributions with application to modeling COVID-19 mortality ratesMutua Kilai0Gichuhi A. Waititu1Wanjoya A. Kibira2Huda M. Alshanbari3M. El-Morshedy4Department of Mathematics, Pan African Insitute of Basic Science, Technology and Innovation, Nairobi, Kenya; Corresponding author.Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, KenyaDepartment of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, KenyaDepartment of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Mathematics, College of Science and humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, EgyptThis paper proposes a new generalization of the Gull Alpha Power Family of distribution, namely the exponentiated generalized gull alpha power family of distribution abbreviated as (EGGAPF) with two additional parameters. This proposed family of distributions has some well known sub-models. Some of the basic properties of the distribution like the hazard function, survival function, order statistics, quantile function, moment generating function are investigated. In order to estimate the parameters of the model the method of maximum likelihood estimation is used. To assess the performance of the MLE estimates a simulation study was performed. It is observed that with increase in sample size, the average bias, and the RMSE decrease. A distribution from this family is fitted to two real data sets and compared to its sub-models. It can be concluded that the proposed distribution outperforms its sub-models.http://www.sciencedirect.com/science/article/pii/S221137972200119XGull Alpha Power FamilyExponentiated generalized distributionQuantile functionCramer–Von Misses testMaximum likelihood estimation |
| spellingShingle | Mutua Kilai Gichuhi A. Waititu Wanjoya A. Kibira Huda M. Alshanbari M. El-Morshedy A new generalization of Gull Alpha Power Family of distributions with application to modeling COVID-19 mortality rates Results in Physics Gull Alpha Power Family Exponentiated generalized distribution Quantile function Cramer–Von Misses test Maximum likelihood estimation |
| title | A new generalization of Gull Alpha Power Family of distributions with application to modeling COVID-19 mortality rates |
| title_full | A new generalization of Gull Alpha Power Family of distributions with application to modeling COVID-19 mortality rates |
| title_fullStr | A new generalization of Gull Alpha Power Family of distributions with application to modeling COVID-19 mortality rates |
| title_full_unstemmed | A new generalization of Gull Alpha Power Family of distributions with application to modeling COVID-19 mortality rates |
| title_short | A new generalization of Gull Alpha Power Family of distributions with application to modeling COVID-19 mortality rates |
| title_sort | new generalization of gull alpha power family of distributions with application to modeling covid 19 mortality rates |
| topic | Gull Alpha Power Family Exponentiated generalized distribution Quantile function Cramer–Von Misses test Maximum likelihood estimation |
| url | http://www.sciencedirect.com/science/article/pii/S221137972200119X |
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