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dc.contributor.supervisor Johnson, Brad C. (Statistics) en
dc.contributor.author Karaganis, Milana
dc.date.accessioned 2009-09-14T21:28:48Z
dc.date.available 2009-09-14T21:28:48Z
dc.date.issued 2009-09-14T21:28:48Z
dc.identifier.uri http://hdl.handle.net/1993/3207
dc.description.abstract Model-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.format.extent 5908856 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.rights info:eu-repo/semantics/openAccess
dc.subject Small-area estimation en
dc.subject Hierarchical Bayes estimation en
dc.subject area-specific geographical effects en
dc.subject loss functions en
dc.subject generalized linear mixed model en
dc.subject weighted balanced-type loss function en
dc.title Small Area Estimation for Survey Data: A Hierarchical Bayes Approach en
dc.type info:eu-repo/semantics/masterThesis
dc.degree.discipline Statistics en
dc.contributor.examiningcommittee Marchand, Éric (mathématiques, Université de Sherbrooke) Leblanc, Alexandre (Statistics) en
dc.degree.level Master of Science (M.Sc.) en
dc.description.note October 2009 en


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