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dc.contributor.authorOnyango, Christopher Ouma
dc.contributor.authorOtieno, Romanus Odhiambo
dc.contributor.authorOrwa, George Otieno
dc.date.accessioned2018-10-26T14:29:21Z
dc.date.accessioned2020-02-06T13:15:10Z
dc.date.available2018-10-26T14:29:21Z
dc.date.available2020-02-06T13:15:10Z
dc.date.issued2010
dc.identifier.urihttp://repository.must.ac.ke/handle/123456789/931
dc.description.abstractChambers and Dorfman (2002) constructed bootstrap confidence intervals in model based estimation for finite population totals assuming that auxiliary values are available throughout a target population and that the auxiliary values are independent. They also assumed that the cluster sizes are known throughout the target population. We now extend to two stage sampling in which the cluster sizes are known only for the sampled clusters, and we therefore predict the unobserved part of the population total. Jan and Elinor (2008) have done similar work, but unlike them, we use a general model, in which the auxiliary values are not necessarily independent. We demonstrate that the asymptotic properties of our proposed estimator and its coverage rates are better than those constructed under the model assisted local polynomial regression model.en_US
dc.language.isoenen_US
dc.publisherPakistan Journal of Statistics and Operation Researchen_US
dc.subjectModel based surveys; robustness; confidence intervalsen_US
dc.titleGENERALISED MODEL BASED CONFIDENCE INTERVALS IN TWO STAGE CLUSTER SAMPLINGen_US
dc.typeArticleen_US


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