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dc.contributor.authorMuhammad Tahiren_US
dc.contributor.authorMuhammad Aslamen_US
dc.contributor.authorZawar Hussainen_US
dc.contributor.authorAkbar Ali Khanen_US
dc.date.accessioned2019-05-07T09:59:47Z-
dc.date.available2019-05-07T09:59:47Z-
dc.date.issued2018en_US
dc.identifier.issn0125-2526en_US
dc.identifier.urihttp://it.science.cmu.ac.th/ejournal/dl.php?journal_id=8993en_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/64100-
dc.description.abstractThis study aims at modeling a heterogeneous population assuming the 3-component mixture of Exponential, Rayleigh and Burr Type-XII distributions using type-I right censored data in Bayesian perspective. The censored sampling situation is considered due to its popularity in reliability theory and survival analysis. The elegant closed form expressions for the Bayes estimators and posterior risks using the non-informative (uniform and Jeffreys’) priors under squared error loss function are derived for censored data as well as for complete sample. In case when no or little prior information is available, elicitation of hyperparameters is given. Some mathematical expressions for different functions of survival time are also derived. To examine, numerically, the performance of the Bayes estimators, we have simulated their statistical properties under different scenarios.en_US
dc.languageEngen_US
dc.publisherScience Faculty of Chiang Mai Universityen_US
dc.titleOn the 3-Component Mixture of Exponential, Rayleigh and Burr Type-XII Distributions: A Simulation Study in Bayesian Frameworken_US
dc.typeบทความวารสารen_US
article.title.sourcetitleChiang Mai Journal of Scienceen_US
article.volume45en_US
article.stream.affiliationsDepartment of Statistics, Government College University, Faisalabad 38000, Pakistan.en_US
article.stream.affiliationsDepartment of Mathematics and Statistics, Riphah International University, Islamabad 44000, Pakistan.en_US
article.stream.affiliationsDepartment of Statistics, Quaid-i-Azam University, Islamabad 44000, Pakistan.en_US
Appears in Collections:CMUL: Journal Articles

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