New Bayesian Estimators for Randomized Response Technique

dc.contributor.authorAdepetun, A. O.
dc.contributor.authorAdewara, A. A.
dc.date.accessioned2023-06-13T10:07:04Z
dc.date.available2023-06-13T10:07:04Z
dc.date.issued2017
dc.description.abstractThis paper proposes new Bayesian estimators of the population proportion of a sensitive attribute when life data were collected through the administration of questionnaires on abortion on 300 matured women in some selected hospitals in Akure, Ondo State, Nigeria. Assuming both the Kumaraswamy (KUMA) and the generalised (GLS) beta distributions as alternative beta priors, efficiency of the proposed Bayesian estimators was established for a wide interval of the values of the population proportion (π). We observed that for small, medium as well as large sample sizes, the developed Bayesian estimators were better in capturing responses from respondents than the conventional simple beta estimator proposed by Hussain and Shabbir (2009a) as π approaches one.en_US
dc.description.sponsorshipselfen_US
dc.identifier.issn0127-9246
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/11126
dc.language.isoenen_US
dc.publisherMalaysian Journal of Applied Sciencesen_US
dc.subjectAlternative beta priors; sensitive attribute; mean square error; absolute bias; efficiency.en_US
dc.titleNew Bayesian Estimators for Randomized Response Techniqueen_US
dc.typeArticleen_US

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