On the Rayleigh-geometric distribution with applications

A two-parameter Rayleigh-geometric distribution with increasing-decreasing-increasing and strictly increasing hazard rate characteristics is reviewed. Various properties are discussed and expressed analytically. The estimation of the distribution parameters is studied by the method of maximum likeli...

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Autores principales: Okorie, Idika E., Akpanta, Anthony C., Ohakwe, Johnson, Chikezie, David C., Onyemachi, Chris U.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6695284/
https://www.ncbi.nlm.nih.gov/pubmed/31428712
http://dx.doi.org/10.1016/j.heliyon.2019.e02200
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author Okorie, Idika E.
Akpanta, Anthony C.
Ohakwe, Johnson
Chikezie, David C.
Onyemachi, Chris U.
author_facet Okorie, Idika E.
Akpanta, Anthony C.
Ohakwe, Johnson
Chikezie, David C.
Onyemachi, Chris U.
author_sort Okorie, Idika E.
collection PubMed
description A two-parameter Rayleigh-geometric distribution with increasing-decreasing-increasing and strictly increasing hazard rate characteristics is reviewed. Various properties are discussed and expressed analytically. The estimation of the distribution parameters is studied by the method of maximum likelihood and validated by a simulation study. Numerical examples based on two real data-sets on the waiting time in queue and CO(2) emissions are given. The Rayleigh-geometric distribution in this paper has a simpler analytical expression compared to the pre-existing distributions with different parameterizations.
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spelling pubmed-66952842019-08-19 On the Rayleigh-geometric distribution with applications Okorie, Idika E. Akpanta, Anthony C. Ohakwe, Johnson Chikezie, David C. Onyemachi, Chris U. Heliyon Article A two-parameter Rayleigh-geometric distribution with increasing-decreasing-increasing and strictly increasing hazard rate characteristics is reviewed. Various properties are discussed and expressed analytically. The estimation of the distribution parameters is studied by the method of maximum likelihood and validated by a simulation study. Numerical examples based on two real data-sets on the waiting time in queue and CO(2) emissions are given. The Rayleigh-geometric distribution in this paper has a simpler analytical expression compared to the pre-existing distributions with different parameterizations. Elsevier 2019-08-05 /pmc/articles/PMC6695284/ /pubmed/31428712 http://dx.doi.org/10.1016/j.heliyon.2019.e02200 Text en © 2019 The Authors. Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Okorie, Idika E.
Akpanta, Anthony C.
Ohakwe, Johnson
Chikezie, David C.
Onyemachi, Chris U.
On the Rayleigh-geometric distribution with applications
title On the Rayleigh-geometric distribution with applications
title_full On the Rayleigh-geometric distribution with applications
title_fullStr On the Rayleigh-geometric distribution with applications
title_full_unstemmed On the Rayleigh-geometric distribution with applications
title_short On the Rayleigh-geometric distribution with applications
title_sort on the rayleigh-geometric distribution with applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6695284/
https://www.ncbi.nlm.nih.gov/pubmed/31428712
http://dx.doi.org/10.1016/j.heliyon.2019.e02200
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