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Activity Number:
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62
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #305296 |
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Title:
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Benchmarking Finite Population Means Using a Bayesian Regression (Student Paper Competitions)
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Author(s):
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Ma Criselda S. Toto*+ and Balgobin Nandram
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Companies:
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Worcester Polytechnic Institute and Medical College of Georgia
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Address:
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100 Institute Rd, Worcester, MA, 01609,
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Keywords:
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Multivariate Normal Density ; Nested-error Regression Model ; Posterior propriety ; Random Samples ; Small area estimation
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Abstract:
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The main goal in small area estimation is to use models to `borrow strength' from the ensemble because the direct estimates of small area parameters are generally unreliable. However, when models are used, the combined estimates from all small areas do not usually match the value of the single estimate on the large area. Benchmarking is done by applying a constraint, internally or externally, that will ensure that the `total' of the small areas matches the 'grand total.' We use a Bayesian nested error regression model to develop a method to benchmark the finite population means of small areas. In two illustrative examples, we apply our method to estimate the number of acres of crop and body mass index. We also perform a simulation study to further assess the properties of our method.
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