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dc.contributor.authorOdongo, L. O.
dc.date.accessioned2014-05-30T08:33:36Z
dc.date.available2014-05-30T08:33:36Z
dc.date.issued2005
dc.identifier.citationJournal of Statistics Vol. 1 (1): pp. 1-8en_US
dc.identifier.issn1117-1421
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/9716
dc.descriptiondoi.org/10.4314/eajosta.v1i1.39150en_US
dc.description.abstractIn this article an application of a kernel based nonparametric approach in constructing a large sample nonparametric confidence interval for a shift parameter is considered. The method is illustrated using the Cauchy distribution as a location model. The kernel-based method is found to have a shorter interval for the shift parameter between two Cauchy distributions than the one based on the Mann-Whitney test statistic.en_US
dc.language.isoenen_US
dc.publisherIfe Centre for Psychological Studiesen_US
dc.subjectBest Asymptotic Normalen_US
dc.subjectCauchy distributionen_US
dc.subjectKernel estimatesen_US
dc.subjectMann-Whitney test statisticen_US
dc.subjectNonparametric confidence intervalen_US
dc.subjectShift parameter.en_US
dc.titleNonparametric Confidence Interval for a Shift Parameter for Cauchy distributionen_US
dc.typeArticleen_US


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