Secant Kumaraswamy Family of Distributions: Properties, Regression Model, and Applications
In this study, Secant Kumaraswamy family of distributions is proposed and studied. This is motivated by the fact that no one distribution can model all types of data from different fields. Therefore, there is the need to develop distributions with desirable properties and flexible enough for modelli...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Computational and Mathematical Methods |
| Online Access: | http://dx.doi.org/10.1155/2024/8925329 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832548361212788736 |
|---|---|
| author | Salifu Nanga Shei Baba Sayibu Irene Dekomwine Angbing Mubarika Alhassan Abdul-Majeed Benson Abdul Ghaniyyu Abubakari Suleman Nasiru |
| author_facet | Salifu Nanga Shei Baba Sayibu Irene Dekomwine Angbing Mubarika Alhassan Abdul-Majeed Benson Abdul Ghaniyyu Abubakari Suleman Nasiru |
| author_sort | Salifu Nanga |
| collection | DOAJ |
| description | In this study, Secant Kumaraswamy family of distributions is proposed and studied. This is motivated by the fact that no one distribution can model all types of data from different fields. Therefore, there is the need to develop distributions with desirable properties and flexible enough for modelling data exhibiting different characteristics. Some properties of the new family of distributions, including the quantile function, moments, moment generating function, and mean residual life function, are derived. Five special cases of the family of distributions are presented, and their flexibility is shown by the varying degrees of skewness and kurtosis and nonmonotonic hazard rates. The maximum likelihood estimation method is used to obtain estimators of the family of distributions. Two location-scale regression models are developed for the Secant Kumaraswamy Weibull distribution, which is a special case of the family of distributions. Six different real datasets are used to demonstrate the usefulness of the family of distributions and the regression models. The results show that the family of distributions can be used to model real datasets. |
| format | Article |
| id | doaj-art-d77024a139dd41a0bb40ea96a69f1ddf |
| institution | Kabale University |
| issn | 2577-7408 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Computational and Mathematical Methods |
| spelling | doaj-art-d77024a139dd41a0bb40ea96a69f1ddf2025-02-03T06:14:52ZengWileyComputational and Mathematical Methods2577-74082024-01-01202410.1155/2024/8925329Secant Kumaraswamy Family of Distributions: Properties, Regression Model, and ApplicationsSalifu Nanga0Shei Baba Sayibu1Irene Dekomwine Angbing2Mubarika Alhassan3Abdul-Majeed Benson4Abdul Ghaniyyu Abubakari5Suleman Nasiru6Department of Basic SciencesDepartment of StatisticsDepartment of Statistics and Actuarial ScienceDepartment of Statistical ScienceStudent Affairs UnitDepartment of Statistics and Actuarial ScienceDepartment of Statistics and Actuarial ScienceIn this study, Secant Kumaraswamy family of distributions is proposed and studied. This is motivated by the fact that no one distribution can model all types of data from different fields. Therefore, there is the need to develop distributions with desirable properties and flexible enough for modelling data exhibiting different characteristics. Some properties of the new family of distributions, including the quantile function, moments, moment generating function, and mean residual life function, are derived. Five special cases of the family of distributions are presented, and their flexibility is shown by the varying degrees of skewness and kurtosis and nonmonotonic hazard rates. The maximum likelihood estimation method is used to obtain estimators of the family of distributions. Two location-scale regression models are developed for the Secant Kumaraswamy Weibull distribution, which is a special case of the family of distributions. Six different real datasets are used to demonstrate the usefulness of the family of distributions and the regression models. The results show that the family of distributions can be used to model real datasets.http://dx.doi.org/10.1155/2024/8925329 |
| spellingShingle | Salifu Nanga Shei Baba Sayibu Irene Dekomwine Angbing Mubarika Alhassan Abdul-Majeed Benson Abdul Ghaniyyu Abubakari Suleman Nasiru Secant Kumaraswamy Family of Distributions: Properties, Regression Model, and Applications Computational and Mathematical Methods |
| title | Secant Kumaraswamy Family of Distributions: Properties, Regression Model, and Applications |
| title_full | Secant Kumaraswamy Family of Distributions: Properties, Regression Model, and Applications |
| title_fullStr | Secant Kumaraswamy Family of Distributions: Properties, Regression Model, and Applications |
| title_full_unstemmed | Secant Kumaraswamy Family of Distributions: Properties, Regression Model, and Applications |
| title_short | Secant Kumaraswamy Family of Distributions: Properties, Regression Model, and Applications |
| title_sort | secant kumaraswamy family of distributions properties regression model and applications |
| url | http://dx.doi.org/10.1155/2024/8925329 |
| work_keys_str_mv | AT salifunanga secantkumaraswamyfamilyofdistributionspropertiesregressionmodelandapplications AT sheibabasayibu secantkumaraswamyfamilyofdistributionspropertiesregressionmodelandapplications AT irenedekomwineangbing secantkumaraswamyfamilyofdistributionspropertiesregressionmodelandapplications AT mubarikaalhassan secantkumaraswamyfamilyofdistributionspropertiesregressionmodelandapplications AT abdulmajeedbenson secantkumaraswamyfamilyofdistributionspropertiesregressionmodelandapplications AT abdulghaniyyuabubakari secantkumaraswamyfamilyofdistributionspropertiesregressionmodelandapplications AT sulemannasiru secantkumaraswamyfamilyofdistributionspropertiesregressionmodelandapplications |