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Inference on the stress strength reliability with exponentiated generalized Marshall Olkin-G distribution

In this paper, an inference on stress-strength reliability model is introduced in case of the exponentiated generalized Marshall Olkin G family of distributions. The maximum likelihood estimator of the stress-strength reliability function is deduced. An asymptotic confidence and bootstrap confidence...

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Autor principal: Salah Youssef Temraz, Neama
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411788/
https://www.ncbi.nlm.nih.gov/pubmed/37556469
http://dx.doi.org/10.1371/journal.pone.0280183
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author Salah Youssef Temraz, Neama
author_facet Salah Youssef Temraz, Neama
author_sort Salah Youssef Temraz, Neama
collection PubMed
description In this paper, an inference on stress-strength reliability model is introduced in case of the exponentiated generalized Marshall Olkin G family of distributions. The maximum likelihood estimator of the stress-strength reliability function is deduced. An asymptotic confidence and bootstrap confidence intervals for the stress-strength reliability function are derived. A Bayesian inference is introduced for the stress-strength reliability. A simulation is introduced to obtain the maximum likelihood and Bayesian estimates for the stress strength reliability. Real data applications are provided to show the results for the stress-strength model and compare the exponentiated generalized Marshall Olkin-G distribution with other distributions.
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spelling pubmed-104117882023-08-10 Inference on the stress strength reliability with exponentiated generalized Marshall Olkin-G distribution Salah Youssef Temraz, Neama PLoS One Research Article In this paper, an inference on stress-strength reliability model is introduced in case of the exponentiated generalized Marshall Olkin G family of distributions. The maximum likelihood estimator of the stress-strength reliability function is deduced. An asymptotic confidence and bootstrap confidence intervals for the stress-strength reliability function are derived. A Bayesian inference is introduced for the stress-strength reliability. A simulation is introduced to obtain the maximum likelihood and Bayesian estimates for the stress strength reliability. Real data applications are provided to show the results for the stress-strength model and compare the exponentiated generalized Marshall Olkin-G distribution with other distributions. Public Library of Science 2023-08-09 /pmc/articles/PMC10411788/ /pubmed/37556469 http://dx.doi.org/10.1371/journal.pone.0280183 Text en © 2023 Neama Salah Youssef Temraz 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
Salah Youssef Temraz, Neama
Inference on the stress strength reliability with exponentiated generalized Marshall Olkin-G distribution
title Inference on the stress strength reliability with exponentiated generalized Marshall Olkin-G distribution
title_full Inference on the stress strength reliability with exponentiated generalized Marshall Olkin-G distribution
title_fullStr Inference on the stress strength reliability with exponentiated generalized Marshall Olkin-G distribution
title_full_unstemmed Inference on the stress strength reliability with exponentiated generalized Marshall Olkin-G distribution
title_short Inference on the stress strength reliability with exponentiated generalized Marshall Olkin-G distribution
title_sort inference on the stress strength reliability with exponentiated generalized marshall olkin-g distribution
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411788/
https://www.ncbi.nlm.nih.gov/pubmed/37556469
http://dx.doi.org/10.1371/journal.pone.0280183
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