Inverse Lomax-Rayleigh distribution with application

In this paper, an extension of Rayleigh distribution called Inverse Lomax Rayleigh (ILR) is proposed by using the Inverse Lomax generator of [13]. Properties of ILR were derived. This includes the complete and incomplete moments, entropy, distribution of order statistics, and quantile function. A si...

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Detalles Bibliográficos
Autores principales: Falgore, Jamilu Yunusa, Isah, Muhammad Nazir, Abdulsalam, Hussein Ahmad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606345/
https://www.ncbi.nlm.nih.gov/pubmed/34841105
http://dx.doi.org/10.1016/j.heliyon.2021.e08383
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author Falgore, Jamilu Yunusa
Isah, Muhammad Nazir
Abdulsalam, Hussein Ahmad
author_facet Falgore, Jamilu Yunusa
Isah, Muhammad Nazir
Abdulsalam, Hussein Ahmad
author_sort Falgore, Jamilu Yunusa
collection PubMed
description In this paper, an extension of Rayleigh distribution called Inverse Lomax Rayleigh (ILR) is proposed by using the Inverse Lomax generator of [13]. Properties of ILR were derived. This includes the complete and incomplete moments, entropy, distribution of order statistics, and quantile function. A simulation study was presented to explore the properties of the estimates. This shows that they are unbiased, consistent, and efficient. An application to fatigue data shows the flexibility of ILR distribution, as it outperforms all the comparators with minimum values of all the measures.
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spelling pubmed-86063452021-11-26 Inverse Lomax-Rayleigh distribution with application Falgore, Jamilu Yunusa Isah, Muhammad Nazir Abdulsalam, Hussein Ahmad Heliyon Research Article In this paper, an extension of Rayleigh distribution called Inverse Lomax Rayleigh (ILR) is proposed by using the Inverse Lomax generator of [13]. Properties of ILR were derived. This includes the complete and incomplete moments, entropy, distribution of order statistics, and quantile function. A simulation study was presented to explore the properties of the estimates. This shows that they are unbiased, consistent, and efficient. An application to fatigue data shows the flexibility of ILR distribution, as it outperforms all the comparators with minimum values of all the measures. Elsevier 2021-11-18 /pmc/articles/PMC8606345/ /pubmed/34841105 http://dx.doi.org/10.1016/j.heliyon.2021.e08383 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Falgore, Jamilu Yunusa
Isah, Muhammad Nazir
Abdulsalam, Hussein Ahmad
Inverse Lomax-Rayleigh distribution with application
title Inverse Lomax-Rayleigh distribution with application
title_full Inverse Lomax-Rayleigh distribution with application
title_fullStr Inverse Lomax-Rayleigh distribution with application
title_full_unstemmed Inverse Lomax-Rayleigh distribution with application
title_short Inverse Lomax-Rayleigh distribution with application
title_sort inverse lomax-rayleigh distribution with application
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606345/
https://www.ncbi.nlm.nih.gov/pubmed/34841105
http://dx.doi.org/10.1016/j.heliyon.2021.e08383
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