Show simple item record

dc.contributor.authorBii, Nelson Kiprono
dc.contributor.authorOnyango, Ouma Christopher
dc.date.accessioned2017-12-28T09:09:59Z
dc.date.available2017-12-28T09:09:59Z
dc.date.issued2017
dc.identifier.citationPak.j.stat.oper.res. Vol.XIII No.1 2017 pp127-139en_US
dc.identifier.issn1816-2711
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/17994
dc.descriptionResearch Articleen_US
dc.description.abstractEstimation of finite population parameters has been an area of concern to statisticians for decades. This paper presents an estimation of the population mean under a model-assisted approach. Dorfman (1992), Breidt and Opsomer (2000) and Ouma et al (2010) carried out the estimation of finite population total on the assumption that the sample size is large and the sampling distribution is approximately normal. Unlike their researches, this paper considered a case when the sample size is small under a model-assisted approach. A model-assisted regression model was considered in a case where the cluster sizes are known only for the sampled clusters in order to predict the unobserved part of the population mean. Under mild assumptions, the proposed estimator is asymptotically unbiased and its conditional error variance tends to zero. Simulation studies show that model assisted estimation performs better than model based estimation of a finite population mean in a case where the sample size is small.en_US
dc.language.isoenen_US
dc.publisherUniversity of the Punjaben_US
dc.subjectModel-assisted surveysen_US
dc.subjectNon-parametric inferenceen_US
dc.subjectTwo-stage cluster samplingen_US
dc.titleModel-Assisted Estimation of Population Mean in Two-Stage Cluster Samplingen_US
dc.typeArticleen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record