Statistical Inference of Odd Fréchet Inverse Lomax Distribution with Applications
In this article, we propose and study a new three-parameter distribution, called the odd Fréchet inverse Lomax (OFIL) distribution, derived by combining the odd Fréchet-G family and the inverse Lomax distribution. Since Fréchet is a continuous distribution with wide applicability in extreme value th...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/4658596 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849308860364357632 |
|---|---|
| author | Ramadan A. ZeinEldin Muhammad Ahsan ul Haq Sharqa Hashmi Mahmoud Elsehety M. Elgarhy |
| author_facet | Ramadan A. ZeinEldin Muhammad Ahsan ul Haq Sharqa Hashmi Mahmoud Elsehety M. Elgarhy |
| author_sort | Ramadan A. ZeinEldin |
| collection | DOAJ |
| description | In this article, we propose and study a new three-parameter distribution, called the odd Fréchet inverse Lomax (OFIL) distribution, derived by combining the odd Fréchet-G family and the inverse Lomax distribution. Since Fréchet is a continuous distribution with wide applicability in extreme value theory, the new model contains these properties as well as the characteristics of the inverse Lomax distribution which make it more flexible and provide a good alternative for some well-known lifetime distributions. We initially present a linear representation of its functions and discussion on density and hazard rate function. Then, we study its various mathematical properties. Different estimation methods are used to estimate parameters of OFIL. The Monte Carlo simulation study is carried out to compare the efficiencies of different methods of estimation. The maximum likelihood estimation (MLE) method is used to estimate the OFIL parameters by considering three practical data applications. We show that the related model is the best in comparisons based on Akaike information criterion (AIC), Bayesian information criterion (BIC), and other goodness-of-fit measures. |
| format | Article |
| id | doaj-art-0d9316bf4e5144b8bb861c57e11bce6e |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0d9316bf4e5144b8bb861c57e11bce6e2025-08-20T03:54:20ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/46585964658596Statistical Inference of Odd Fréchet Inverse Lomax Distribution with ApplicationsRamadan A. ZeinEldin0Muhammad Ahsan ul Haq1Sharqa Hashmi2Mahmoud Elsehety3M. Elgarhy4Deanship of Scientific Research, King Abdulaziz University, Jeddah, Saudi ArabiaCollege of Statistical & Actuarial Sciences, University of the Punjab, Lahore, PakistanCollege of Statistical & Actuarial Sciences, University of the Punjab, Lahore, PakistanKing Abdulaziz University, Jeddah, Saudi ArabiaValley High Institute for Management Finance and Information Systems, Obour, Qaliubia 11828, EgyptIn this article, we propose and study a new three-parameter distribution, called the odd Fréchet inverse Lomax (OFIL) distribution, derived by combining the odd Fréchet-G family and the inverse Lomax distribution. Since Fréchet is a continuous distribution with wide applicability in extreme value theory, the new model contains these properties as well as the characteristics of the inverse Lomax distribution which make it more flexible and provide a good alternative for some well-known lifetime distributions. We initially present a linear representation of its functions and discussion on density and hazard rate function. Then, we study its various mathematical properties. Different estimation methods are used to estimate parameters of OFIL. The Monte Carlo simulation study is carried out to compare the efficiencies of different methods of estimation. The maximum likelihood estimation (MLE) method is used to estimate the OFIL parameters by considering three practical data applications. We show that the related model is the best in comparisons based on Akaike information criterion (AIC), Bayesian information criterion (BIC), and other goodness-of-fit measures.http://dx.doi.org/10.1155/2020/4658596 |
| spellingShingle | Ramadan A. ZeinEldin Muhammad Ahsan ul Haq Sharqa Hashmi Mahmoud Elsehety M. Elgarhy Statistical Inference of Odd Fréchet Inverse Lomax Distribution with Applications Complexity |
| title | Statistical Inference of Odd Fréchet Inverse Lomax Distribution with Applications |
| title_full | Statistical Inference of Odd Fréchet Inverse Lomax Distribution with Applications |
| title_fullStr | Statistical Inference of Odd Fréchet Inverse Lomax Distribution with Applications |
| title_full_unstemmed | Statistical Inference of Odd Fréchet Inverse Lomax Distribution with Applications |
| title_short | Statistical Inference of Odd Fréchet Inverse Lomax Distribution with Applications |
| title_sort | statistical inference of odd frechet inverse lomax distribution with applications |
| url | http://dx.doi.org/10.1155/2020/4658596 |
| work_keys_str_mv | AT ramadanazeineldin statisticalinferenceofoddfrechetinverselomaxdistributionwithapplications AT muhammadahsanulhaq statisticalinferenceofoddfrechetinverselomaxdistributionwithapplications AT sharqahashmi statisticalinferenceofoddfrechetinverselomaxdistributionwithapplications AT mahmoudelsehety statisticalinferenceofoddfrechetinverselomaxdistributionwithapplications AT melgarhy statisticalinferenceofoddfrechetinverselomaxdistributionwithapplications |