Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2179
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dc.contributor.authorSen, Subhradev-
dc.contributor.authorAfify, Ahmed Z-
dc.contributor.authorAl-Mofleh, Hazem-
dc.contributor.authorAhsanullah, Mohammad-
dc.date.accessioned2023-12-06T10:14:28Z-
dc.date.available2023-12-06T10:14:28Z-
dc.date.issued2019-
dc.identifier.citationVol. 33, No. 16; pp. 5291-5330en_US
dc.identifier.issn0354-5180-
dc.identifier.urihttps://doi.org/10.2298/FIL1916291S-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2179-
dc.description.abstractIn this paper, a new probability distribution, which is synthesized based on the quasi xgamma [26] and geometric distributions, is proposed and studied. The proposed distribution so synthesized is basically a family of positively skewed probability distributions and possesses increasing and decreasing hazard rate properties depending on the values of the unknown parameters. Different important distributional and survival and/or reliability properties are also studied. A unique characterization of the distribution is presented based on reversed hazard rate. Seven different frequentist methods of estimating unknown parameters are proposed and the methods are justified with Monte-Carlo simulation study. Flexible data generation algorithm eases the utility of the proposed model in survival and/or reliability application which is accomplished by real data analyses and by comparing with other competitive life distributions. © 2019, University of Nis. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherFilomaten_US
dc.subjectCharacterizationen_US
dc.subjectGeometric distributionen_US
dc.subjectLife distributionsen_US
dc.subjectMaximum likelihood estimationen_US
dc.titleThe Quasi Xgamma-Geometric Distribution With Application In Medicineen_US
dc.typeArticleen_US
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