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Progressive censoring schemes for marshall-olkin pareto distribution with applications: Estimation and prediction

In this paper two prediction methods are used to predict the non-observed (censored) units under progressive Type-II censored samples. The lifetimes of the units follow Marshall-Olkin Pareto distribution. We observe the posterior predictive density of the non-observed units and construct predictive...

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Autores principales: Alshenawy, R., Haj Ahmad, Hanan, Al-Alwan, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9328570/
https://www.ncbi.nlm.nih.gov/pubmed/35895723
http://dx.doi.org/10.1371/journal.pone.0270750
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author Alshenawy, R.
Haj Ahmad, Hanan
Al-Alwan, Ali
author_facet Alshenawy, R.
Haj Ahmad, Hanan
Al-Alwan, Ali
author_sort Alshenawy, R.
collection PubMed
description In this paper two prediction methods are used to predict the non-observed (censored) units under progressive Type-II censored samples. The lifetimes of the units follow Marshall-Olkin Pareto distribution. We observe the posterior predictive density of the non-observed units and construct predictive intervals as well. Furthermore, we provide inference on the unknown parameters of the Marshall-Olkin model, so we observe point and interval estimation by using maximum likelihood and Bayesian estimation methods. Bayes estimation methods are obtained under quadratic loss function. EM algorithm is used to obtain numerical values of the Maximum likelihood method and Gibbs and the Monte Carlo Markov chain techniques are utilized for Bayesian calculations. A simulation study is performed to evaluate the performance of the estimators with respect to the mean square errors and the biases. Finally, we find the best prediction method by implementing a real data example under progressive Type-II censoring schemes.
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spelling pubmed-93285702022-07-28 Progressive censoring schemes for marshall-olkin pareto distribution with applications: Estimation and prediction Alshenawy, R. Haj Ahmad, Hanan Al-Alwan, Ali PLoS One Research Article In this paper two prediction methods are used to predict the non-observed (censored) units under progressive Type-II censored samples. The lifetimes of the units follow Marshall-Olkin Pareto distribution. We observe the posterior predictive density of the non-observed units and construct predictive intervals as well. Furthermore, we provide inference on the unknown parameters of the Marshall-Olkin model, so we observe point and interval estimation by using maximum likelihood and Bayesian estimation methods. Bayes estimation methods are obtained under quadratic loss function. EM algorithm is used to obtain numerical values of the Maximum likelihood method and Gibbs and the Monte Carlo Markov chain techniques are utilized for Bayesian calculations. A simulation study is performed to evaluate the performance of the estimators with respect to the mean square errors and the biases. Finally, we find the best prediction method by implementing a real data example under progressive Type-II censoring schemes. Public Library of Science 2022-07-27 /pmc/articles/PMC9328570/ /pubmed/35895723 http://dx.doi.org/10.1371/journal.pone.0270750 Text en © 2022 Alshenawy 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
Alshenawy, R.
Haj Ahmad, Hanan
Al-Alwan, Ali
Progressive censoring schemes for marshall-olkin pareto distribution with applications: Estimation and prediction
title Progressive censoring schemes for marshall-olkin pareto distribution with applications: Estimation and prediction
title_full Progressive censoring schemes for marshall-olkin pareto distribution with applications: Estimation and prediction
title_fullStr Progressive censoring schemes for marshall-olkin pareto distribution with applications: Estimation and prediction
title_full_unstemmed Progressive censoring schemes for marshall-olkin pareto distribution with applications: Estimation and prediction
title_short Progressive censoring schemes for marshall-olkin pareto distribution with applications: Estimation and prediction
title_sort progressive censoring schemes for marshall-olkin pareto distribution with applications: estimation and prediction
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9328570/
https://www.ncbi.nlm.nih.gov/pubmed/35895723
http://dx.doi.org/10.1371/journal.pone.0270750
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