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A new extended gumbel distribution: Properties and application
A robust generalisation of the Gumbel distribution is proposed in this article. This family of distributions is based on the T-X paradigm. From a list of special distributions that have evolved as a result of this family, three separate models are also mentioned in this article. A linear combination...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140309/ https://www.ncbi.nlm.nih.gov/pubmed/35622822 http://dx.doi.org/10.1371/journal.pone.0267142 |
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author | Fayomi, Aisha Khan, Sadaf Tahir, Muhammad Hussain Algarni, Ali Jamal, Farrukh Abu-Shanab, Reman |
author_facet | Fayomi, Aisha Khan, Sadaf Tahir, Muhammad Hussain Algarni, Ali Jamal, Farrukh Abu-Shanab, Reman |
author_sort | Fayomi, Aisha |
collection | PubMed |
description | A robust generalisation of the Gumbel distribution is proposed in this article. This family of distributions is based on the T-X paradigm. From a list of special distributions that have evolved as a result of this family, three separate models are also mentioned in this article. A linear combination of generalised exponential distributions can be used to characterise the density of a new family, which is critical in assessing some of the family’s properties. The statistical features of this family are determined, including exact formulations for the quantile function, ordinary and incomplete moments, generating function, and order statistics. The model parameters are estimated using the maximum likelihood method. Further, one of the unique models has been systematically studied. Along with conventional skewness measures, MacGillivray skewness is also used to quantify the skewness measure. The new probability distribution also enables us to determine certain critical risk indicators, both numerically and graphically. We use a simulated assessment of the suggested distribution, as well as apply three real-world data sets in modelling the proposed model, in order to ensure its authenticity and superiority. |
format | Online Article Text |
id | pubmed-9140309 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91403092022-05-28 A new extended gumbel distribution: Properties and application Fayomi, Aisha Khan, Sadaf Tahir, Muhammad Hussain Algarni, Ali Jamal, Farrukh Abu-Shanab, Reman PLoS One Research Article A robust generalisation of the Gumbel distribution is proposed in this article. This family of distributions is based on the T-X paradigm. From a list of special distributions that have evolved as a result of this family, three separate models are also mentioned in this article. A linear combination of generalised exponential distributions can be used to characterise the density of a new family, which is critical in assessing some of the family’s properties. The statistical features of this family are determined, including exact formulations for the quantile function, ordinary and incomplete moments, generating function, and order statistics. The model parameters are estimated using the maximum likelihood method. Further, one of the unique models has been systematically studied. Along with conventional skewness measures, MacGillivray skewness is also used to quantify the skewness measure. The new probability distribution also enables us to determine certain critical risk indicators, both numerically and graphically. We use a simulated assessment of the suggested distribution, as well as apply three real-world data sets in modelling the proposed model, in order to ensure its authenticity and superiority. Public Library of Science 2022-05-27 /pmc/articles/PMC9140309/ /pubmed/35622822 http://dx.doi.org/10.1371/journal.pone.0267142 Text en © 2022 Fayomi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Fayomi, Aisha Khan, Sadaf Tahir, Muhammad Hussain Algarni, Ali Jamal, Farrukh Abu-Shanab, Reman A new extended gumbel distribution: Properties and application |
title | A new extended gumbel distribution: Properties and application |
title_full | A new extended gumbel distribution: Properties and application |
title_fullStr | A new extended gumbel distribution: Properties and application |
title_full_unstemmed | A new extended gumbel distribution: Properties and application |
title_short | A new extended gumbel distribution: Properties and application |
title_sort | new extended gumbel distribution: properties and application |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140309/ https://www.ncbi.nlm.nih.gov/pubmed/35622822 http://dx.doi.org/10.1371/journal.pone.0267142 |
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