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dc.contributor.authorKeto, Mauno
dc.contributor.authorPahkinen, Erkki
dc.date.accessioned2017-08-07T09:57:31Z
dc.date.available2017-08-07T09:57:31Z
dc.date.issued2017
dc.identifier.citationKeto, M., & Pahkinen, E. (2017). Sample allocation for efficient model-based small area estimation. <i>Survey Methodology</i>, <i>43</i>(1), 93-106. <a href="http://www.statcan.gc.ca/pub/12-001-x/2017001/article/14817-eng.pdf" target="_blank">http://www.statcan.gc.ca/pub/12-001-x/2017001/article/14817-eng.pdf</a>
dc.identifier.otherCONVID_27137502
dc.identifier.otherTUTKAID_74546
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/55001
dc.description.abstractWe present research results on sample allocations for efficient model-based small area estimation in cases where the areas of interest coincide with the strata. Although model-assisted and model-based estimation methods are common in the production of small area statistics, utilization of the underlying model and estimation method are rarely included in the sample area allocation scheme. Therefore, we have developed a new model-based allocation named g1-allocation. For comparison, one recently developed model-assisted allocation is presented. These two allocations are based on an adjusted measure of homogeneity which is computed using an auxiliary variable and is an approximation of the intra-class correlation within areas. Five model-free area allocation solutions presented in the past are selected from the literature as reference allocations. Equal and proportional allocations need the number of areas and area-specific numbers of basic statistical units. The Neyman, Bankier and NLP (Non-Linear Programming) allocation need values for the study variable concerning area level parameters such as standard deviation, coefficient of variation or totals. In general, allocation methods can be classified according to the optimization criteria and use of auxiliary data. Statistical properties of the various methods are assessed through sample simulation experiments using real population register data. It can be concluded from simulation results that inclusion of the model and estimation method into the allocation method improves estimation results.
dc.language.isoeng
dc.publisherStatistics Canada
dc.relation.ispartofseriesSurvey Methodology
dc.relation.urihttp://www.statcan.gc.ca/pub/12-001-x/2017001/article/14817-eng.pdf
dc.subject.otheroptimal area sample size
dc.subject.otherauxiliary information
dc.subject.othermeasure of homogeneity
dc.titleSample allocation for efficient model-based small area estimation
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201708043414
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineStatisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2017-08-04T09:15:04Z
dc.type.coarjournal article
dc.description.reviewstatuspeerReviewed
dc.format.pagerange93-106
dc.relation.issn0714-0045
dc.relation.numberinseries1
dc.relation.volume43
dc.type.versionpublishedVersion
dc.rights.copyright© Minister of Industry, 2017. Use of this publication is governed by the Statistics Canada Open Licence Agreement.
dc.rights.accesslevelopenAccessfi
dc.subject.ysokriteerit
jyx.subject.urihttp://www.yso.fi/onto/yso/p7607
dc.rights.urlhttp://www.statcan.gc.ca/eng/reference/licence


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Except where otherwise noted, this item's license is described as © Minister of Industry, 2017. Use of this publication is governed by the Statistics Canada Open Licence Agreement.