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Title: Small Area Estimation for Survey Data: A Hierarchical Bayes Approach
Authors: Karaganis, Milana
Supervisor: Johnson, Brad C. (Statistics)
Examining Committee: Marchand, Éric (mathématiques, Université de Sherbrooke) Leblanc, Alexandre (Statistics)
Graduation Date: October 2009
Keywords: Small-area estimation
Hierarchical Bayes estimation
area-specific geographical effects
loss functions
generalized linear mixed model
weighted balanced-type loss function
Issue Date: 14-Sep-2009
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.
Appears in Collection(s):FGS - Electronic Theses & Dissertations (Public)

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