The Transmuted Odd Fréchet-G Family of Distributions: Theory and Applications
The last years, the odd Fréchet-G family has been considered with success in various statistical applications. This notoriety can be explained by its simple and flexible exponential-odd structure quite different to the other existing families, with the use of only one additional parameter. In counte...
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MDPI AG
2020-06-01
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author | Majdah M. Badr Ibrahim Elbatal Farrukh Jamal Christophe Chesneau Mohammed Elgarhy |
author_facet | Majdah M. Badr Ibrahim Elbatal Farrukh Jamal Christophe Chesneau Mohammed Elgarhy |
author_sort | Majdah M. Badr |
collection | DOAJ |
description | The last years, the odd Fréchet-G family has been considered with success in various statistical applications. This notoriety can be explained by its simple and flexible exponential-odd structure quite different to the other existing families, with the use of only one additional parameter. In counter part, some of its statistical properties suffer of a lack of adaptivity in the sense that they really depend on the choice of the baseline distribution. Hence, efforts have been made to relax this subjectivity by investigating extensions or generalizations of the odd transformation at the heart of the construction of this family, with the aim to reach new perspectives of applications as well. This study explores another possibility, based on the transformation of the whole cumulative distribution function of this family (while keeping the odd transformation intact), through the use of the quadratic rank transmutation that has proven itself in other contexts. We thus introduce and study a new family of flexible distributions called the transmuted odd Fréchet-G family. We show how the former odd Fréchet-G family is enriched by the proposed transformation through theoretical and practical results. We emphasize the special distribution based on the standard exponential distribution because of its desirable features for the statistical modeling. In particular, different kinds of monotonic and nonmonotonic shapes for the probability density and hazard rate functions are observed. Then, we show how the new family can be used in practice. We discuss in detail the parametric estimation of a special model, along with a simulation study. Practical data sets are handle with quite favorable results for the new modeling strategy. |
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language | English |
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spelling | doaj.art-b62ee64f6d42455095d7ab4eaa5f9b2b2023-12-03T11:55:13ZengMDPI AGMathematics2227-73902020-06-018695810.3390/math8060958The Transmuted Odd Fréchet-G Family of Distributions: Theory and ApplicationsMajdah M. Badr0Ibrahim Elbatal1Farrukh Jamal2Christophe Chesneau3Mohammed Elgarhy4Statistics Department, Faculty of Science for Girls, University of Jeddah, P. O. Box 70973, Jeddah 21577, Saudi ArabiaDepartment of Mathematics and Statistics, College of Science Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi ArabiaDepartment of Statistics, Govt. S.A Postgraduate College Dera Nawab Sahib, Bahawalpur, Punjab 63100, PakistanDepartment of Mathematics, Université de Caen, LMNO, Campus II, Science 3, 14032 Caen, FranceValley High Institute for Management Finance and Information Systems, Obour, Qaliubia 11828, EgyptThe last years, the odd Fréchet-G family has been considered with success in various statistical applications. This notoriety can be explained by its simple and flexible exponential-odd structure quite different to the other existing families, with the use of only one additional parameter. In counter part, some of its statistical properties suffer of a lack of adaptivity in the sense that they really depend on the choice of the baseline distribution. Hence, efforts have been made to relax this subjectivity by investigating extensions or generalizations of the odd transformation at the heart of the construction of this family, with the aim to reach new perspectives of applications as well. This study explores another possibility, based on the transformation of the whole cumulative distribution function of this family (while keeping the odd transformation intact), through the use of the quadratic rank transmutation that has proven itself in other contexts. We thus introduce and study a new family of flexible distributions called the transmuted odd Fréchet-G family. We show how the former odd Fréchet-G family is enriched by the proposed transformation through theoretical and practical results. We emphasize the special distribution based on the standard exponential distribution because of its desirable features for the statistical modeling. In particular, different kinds of monotonic and nonmonotonic shapes for the probability density and hazard rate functions are observed. Then, we show how the new family can be used in practice. We discuss in detail the parametric estimation of a special model, along with a simulation study. Practical data sets are handle with quite favorable results for the new modeling strategy.https://www.mdpi.com/2227-7390/8/6/958transmuted familyodd Fréchet-G familymomentsmaximum likelihood estimationconfidence intervalsdata analysis |
spellingShingle | Majdah M. Badr Ibrahim Elbatal Farrukh Jamal Christophe Chesneau Mohammed Elgarhy The Transmuted Odd Fréchet-G Family of Distributions: Theory and Applications Mathematics transmuted family odd Fréchet-G family moments maximum likelihood estimation confidence intervals data analysis |
title | The Transmuted Odd Fréchet-G Family of Distributions: Theory and Applications |
title_full | The Transmuted Odd Fréchet-G Family of Distributions: Theory and Applications |
title_fullStr | The Transmuted Odd Fréchet-G Family of Distributions: Theory and Applications |
title_full_unstemmed | The Transmuted Odd Fréchet-G Family of Distributions: Theory and Applications |
title_short | The Transmuted Odd Fréchet-G Family of Distributions: Theory and Applications |
title_sort | transmuted odd frechet g family of distributions theory and applications |
topic | transmuted family odd Fréchet-G family moments maximum likelihood estimation confidence intervals data analysis |
url | https://www.mdpi.com/2227-7390/8/6/958 |
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