Small Area Estimation for Survey Data: A Hierarchical Bayes Approach

dc.contributor.authorKaraganis, Milana
dc.contributor.examiningcommitteeMarchand, Éric (mathématiques, Université de Sherbrooke) Leblanc, Alexandre (Statistics)en
dc.contributor.supervisorJohnson, Brad C. (Statistics)en
dc.date.accessioned2009-09-14T21:28:48Z
dc.date.available2009-09-14T21:28:48Z
dc.date.issued2009-09-14T21:28:48Z
dc.degree.disciplineStatisticsen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractModel-based estimation techniques have been widely used in small area estimation. This thesis focuses on the Hierarchical Bayes (HB) estimation techniques in application to small area estimation for survey data. We will study the impact of applying spatial structure to area-specific effects and utilizing a specific generalized linear mixed model in comparison with a traditional Fay-Herriot estimation model. We will also analyze different loss functions with applications to a small area estimation problem and compare estimates obtained under these loss functions. Overall, for the case study under consideration, area-specific geographical effects will be shown to have a significant effect on estimates. As well, using a generalized linear mixed model will prove to be more advantageous than the usual Fay-Herriot model. We will also demonstrate the benefits of using a weighted balanced-type loss function for the purpose of balancing the precision of estimates with their closeness to the direct estimates.en
dc.description.noteOctober 2009en
dc.format.extent5908856 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1993/3207
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectSmall-area estimationen
dc.subjectHierarchical Bayes estimationen
dc.subjectarea-specific geographical effectsen
dc.subjectloss functionsen
dc.subjectgeneralized linear mixed modelen
dc.subjectweighted balanced-type loss functionen
dc.titleSmall Area Estimation for Survey Data: A Hierarchical Bayes Approachen
dc.typemaster thesisen_US
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