Classical and Bayesian Inference under Burr-X Distribution Based on New Unified Progressive Hybrid Censoring Scheme with Engineering Applications

In this paper, a new unified progressive hybrid censoring scheme UPHCS has been constructed. This unified censoring scheme covers eleven famous censoring schemes. The estimation problem of Burr-X distribution parameters has been studied using the maximum likelihood and Bayes approaches based on the...

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Main Authors: Saieed F. Ateya, Randa Alharbi, Mutua Kilai, Ramy Aldallal
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Mathematics
Online Access:http://dx.doi.org/10.1155/2022/3746821
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author Saieed F. Ateya
Randa Alharbi
Mutua Kilai
Ramy Aldallal
author_facet Saieed F. Ateya
Randa Alharbi
Mutua Kilai
Ramy Aldallal
author_sort Saieed F. Ateya
collection DOAJ
description In this paper, a new unified progressive hybrid censoring scheme UPHCS has been constructed. This unified censoring scheme covers eleven famous censoring schemes. The estimation problem of Burr-X distribution parameters has been studied using the maximum likelihood and Bayes approaches based on the suggested unified progressive hybrid censored samples. Two real data sets have been used as illustrative engineering examples.
format Article
id doaj-art-180902b26a9243289c41fcc548a8cebb
institution Kabale University
issn 2314-4785
language English
publishDate 2022-01-01
publisher Wiley
record_format Article
series Journal of Mathematics
spelling doaj-art-180902b26a9243289c41fcc548a8cebb2025-02-03T05:51:01ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/3746821Classical and Bayesian Inference under Burr-X Distribution Based on New Unified Progressive Hybrid Censoring Scheme with Engineering ApplicationsSaieed F. Ateya0Randa Alharbi1Mutua Kilai2Ramy Aldallal3Department of MathematicsDepartment of StatisticsDepartment of MathematicsCollege of Business Administration in Hotat Bani TamimIn this paper, a new unified progressive hybrid censoring scheme UPHCS has been constructed. This unified censoring scheme covers eleven famous censoring schemes. The estimation problem of Burr-X distribution parameters has been studied using the maximum likelihood and Bayes approaches based on the suggested unified progressive hybrid censored samples. Two real data sets have been used as illustrative engineering examples.http://dx.doi.org/10.1155/2022/3746821
spellingShingle Saieed F. Ateya
Randa Alharbi
Mutua Kilai
Ramy Aldallal
Classical and Bayesian Inference under Burr-X Distribution Based on New Unified Progressive Hybrid Censoring Scheme with Engineering Applications
Journal of Mathematics
title Classical and Bayesian Inference under Burr-X Distribution Based on New Unified Progressive Hybrid Censoring Scheme with Engineering Applications
title_full Classical and Bayesian Inference under Burr-X Distribution Based on New Unified Progressive Hybrid Censoring Scheme with Engineering Applications
title_fullStr Classical and Bayesian Inference under Burr-X Distribution Based on New Unified Progressive Hybrid Censoring Scheme with Engineering Applications
title_full_unstemmed Classical and Bayesian Inference under Burr-X Distribution Based on New Unified Progressive Hybrid Censoring Scheme with Engineering Applications
title_short Classical and Bayesian Inference under Burr-X Distribution Based on New Unified Progressive Hybrid Censoring Scheme with Engineering Applications
title_sort classical and bayesian inference under burr x distribution based on new unified progressive hybrid censoring scheme with engineering applications
url http://dx.doi.org/10.1155/2022/3746821
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AT randaalharbi classicalandbayesianinferenceunderburrxdistributionbasedonnewunifiedprogressivehybridcensoringschemewithengineeringapplications
AT mutuakilai classicalandbayesianinferenceunderburrxdistributionbasedonnewunifiedprogressivehybridcensoringschemewithengineeringapplications
AT ramyaldallal classicalandbayesianinferenceunderburrxdistributionbasedonnewunifiedprogressivehybridcensoringschemewithengineeringapplications