JSM 2005 - Toronto

Abstract #304426

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Activity Number: 527
Type: Contributed
Date/Time: Thursday, August 11, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Survey Research Methods
Abstract - #304426
Title: Transformation Usage and Model Strategies in Small-area Estimation
Author(s): Trivellore Raghunathan*+ and Dawei Xie
Companies: University of Michigan and University of Pennsylvania
Address: 426 Thompson Street, Ann Arbor, MI, 48104-1248, United States
Keywords: area model ; Fay-Herriot ; Bayesian Hierarchical model ; Metropolis-Hastings ; Gibbs sampling
Abstract:

When estimating mean of binary outcomes in small areas, one could use a binomial-normal two-stage model. It does not make assumptions on the direct estimates of the area means, but the computation could be intensive. An alternative is to transform the direct estimate of area mean and assume a normal-normal two-stage model. These transformations generally perform well when area sample size is large. The performance is unknown when area sample size is small. We compare the performance of these model strategies with respect to its point estimates, variance estimates, and computation intensity. Transformations considered are arc, sin, square root, and logit. We apply different model strategies to the 2000 BRFSS data.

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