Bayesian Estimation of Kumaraswamy Distribution under Different Loss Functions.

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dc.contributor.authorAdegoke, T. M.
dc.contributor.authorNasiri, P
dc.contributor.authorAdegoke, G. K.
dc.contributor.authorYahya, Waheed
dc.contributor.authorAfolayan, Razaq
dc.date.accessioned2021-10-12T11:38:23Z
dc.date.available2021-10-12T11:38:23Z
dc.date.issued2019
dc.description.abstractIn this study, the procedures of Bayesian estimation of the shape parameter of the Kumaraswamy distribution under different prior distributions are examined. The shape parameter of the Kumaraswamy distribution is assumed to follow noninformative prior distributions (such as the extension of Jeffrey’s prior distribution, Hartigan Prior distribution, and Uniform Prior distribution) and the informative prior distribution (such as the Gamma distribution and Inverted levy distribution) were adopted in this work. We compared the obtained estimates using their mean square errors under different loss functions (such as precautionary loss function, Squared error loss function, and Albayyati’s loss function). The results showed that the behaviour of the Bayesian estimations of the shape parameter of the Kumaraswamy distribution under the Squared Error loss function using Inverted levy prior distribution is the best among all the prior distributions considered.en_US
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/6589
dc.language.isoenen_US
dc.publisherProfessional Statisticians Society of Nigeria(PSSN).en_US
dc.relation.ispartofseriesVol. 2;90-102
dc.subjectGamma distributionen_US
dc.subjectJeffrey’s prioren_US
dc.subjectKumaraswamy distributionen_US
dc.subjectHartigan Prioren_US
dc.subjectInverted levy distributionen_US
dc.subjectUniform Prioren_US
dc.subjectMean Square Erroren_US
dc.subjectLoss Function.en_US
dc.titleBayesian Estimation of Kumaraswamy Distribution under Different Loss Functions.en_US
dc.typeArticleen_US

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