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      Statistical inference for a constant-stress partially accelerated life tests based on progressively hybrid censored samples from inverted Kumaraswamy distribution

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      PLoS ONE
      Public Library of Science

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          Abstract

          In this article, we investigate the problem of point and interval estimations under constant-stress partially accelerated life tests. The lifetime of items under use condition is assumed to follow the two-parameter inverted Kumaraswamy distribution. Based on Type-I progressively hybrid censored samples, the maximum likelihood and Bayesian methods are applied to estimate the model parameters as well as the acceleration factor. Under linear exponential, general entropy and squared error loss functions, Bayesian method outcomes are obtained. In addition, interval estimation is achieved by finding approximately confidence intervals for the parameters, as well as credible intervals. To investigate the accuracy of the obtained estimates and to compare the performance of confidence intervals, a Monte Carlo simulation is developed. Finally, a set of real data is analyzed to demonstrate the estimation procedures.

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          Bayesian Theory

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            Analysis of Type-II progressively hybrid censored data

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              Bayesian Estimation and Prediction Using Asymmetric Loss Functions

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Funding acquisitionRole: Project administrationRole: SoftwareRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: SoftwareRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2022
                1 August 2022
                : 17
                : 8
                Affiliations
                [1 ] Department of Mathematics, Faculty of Science, New Valley University, EL-Khargah, Egypt
                [2 ] Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
                [3 ] Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni-Suef, Egypt
                Universidad Rey Juan Carlos, SPAIN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-22-03714
                10.1371/journal.pone.0272378
                9342795
                35913958
                3c10d1eb-5b2f-483c-a4eb-1a6daedfe67d
                © 2022 Yousef et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 3, Tables: 12, Pages: 24
                Product
                Funding
                Funded by: Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia
                Award ID: IFP-IMSIU202108
                Award Recipient :
                Funding: The Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia (Project number IFP-IMSIU202108). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Research and analysis methods
                Mathematical and statistical techniques
                Statistical methods
                Monte Carlo method
                Physical sciences
                Mathematics
                Statistics
                Statistical methods
                Monte Carlo method
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Bayesian Method
                Engineering and Technology
                Industrial Engineering
                Reliability Engineering
                Reliability
                Physical Sciences
                Mathematics
                Probability Theory
                Random Variables
                Physical Sciences
                Mathematics
                Probability Theory
                Statistical Distributions
                Physical Sciences
                Physics
                Thermodynamics
                Entropy
                Physical Sciences
                Mathematics
                Probability Theory
                Probability Distribution
                Physical Sciences
                Mathematics
                Probability Theory
                Statistical Distributions
                Distribution Curves
                Custom metadata
                The data are used to support the findings of this study are included within the paper.

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                Uncategorized

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