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E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme
In this paper, E-Bayesian estimation of the scale parameter, reliability and hazard rate functions of Chen distribution are considered when a sample is obtained from a type-I censoring scheme. The E-Bayesian estimators are obtained based on the balanced squared error loss function and using the gamm...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517171/ https://www.ncbi.nlm.nih.gov/pubmed/33286408 http://dx.doi.org/10.3390/e22060636 |
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author | Algarni, Ali Almarashi, Abdullah M. Okasha, Hassan Ng, Hon Keung Tony |
author_facet | Algarni, Ali Almarashi, Abdullah M. Okasha, Hassan Ng, Hon Keung Tony |
author_sort | Algarni, Ali |
collection | PubMed |
description | In this paper, E-Bayesian estimation of the scale parameter, reliability and hazard rate functions of Chen distribution are considered when a sample is obtained from a type-I censoring scheme. The E-Bayesian estimators are obtained based on the balanced squared error loss function and using the gamma distribution as a conjugate prior for the unknown scale parameter. Also, the E-Bayesian estimators are derived using three different distributions for the hyper-parameters. Some properties of E-Bayesian estimators based on balanced squared error loss function are discussed. A simulation study is performed to compare the efficiencies of different estimators in terms of minimum mean squared errors. Finally, a real data set is analyzed to illustrate the applicability of the proposed estimators. |
format | Online Article Text |
id | pubmed-7517171 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75171712020-11-09 E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme Algarni, Ali Almarashi, Abdullah M. Okasha, Hassan Ng, Hon Keung Tony Entropy (Basel) Article In this paper, E-Bayesian estimation of the scale parameter, reliability and hazard rate functions of Chen distribution are considered when a sample is obtained from a type-I censoring scheme. The E-Bayesian estimators are obtained based on the balanced squared error loss function and using the gamma distribution as a conjugate prior for the unknown scale parameter. Also, the E-Bayesian estimators are derived using three different distributions for the hyper-parameters. Some properties of E-Bayesian estimators based on balanced squared error loss function are discussed. A simulation study is performed to compare the efficiencies of different estimators in terms of minimum mean squared errors. Finally, a real data set is analyzed to illustrate the applicability of the proposed estimators. MDPI 2020-06-08 /pmc/articles/PMC7517171/ /pubmed/33286408 http://dx.doi.org/10.3390/e22060636 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Algarni, Ali Almarashi, Abdullah M. Okasha, Hassan Ng, Hon Keung Tony E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme |
title | E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme |
title_full | E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme |
title_fullStr | E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme |
title_full_unstemmed | E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme |
title_short | E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme |
title_sort | e-bayesian estimation of chen distribution based on type-i censoring scheme |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517171/ https://www.ncbi.nlm.nih.gov/pubmed/33286408 http://dx.doi.org/10.3390/e22060636 |
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