Abstract #302156

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JSM 2003 Abstract #302156
Activity Number: 53
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #302156
Title: Measurement Error Models for Small Area Estimation
Author(s): Karabi Sinha*+ and Malay Ghosh
Companies: University of Florida and University of Florida
Address: 204 Griffin-Floyd Hall, Gainesville, FL, 32611-8545,
Keywords: small area easimation ; measurement error ; hierarchical Bayes
Abstract:

Direct survey estimators of small areas are usually not very reliable, being accompanied with large standard errors and coefficients of variation. To meet the need for finding indirect small area estimators, a rich collection of models, either explicit or implicit, have been proposed and studied over the past few years. However, it appears that measurement error models, in spite of many other applications, have hardly been used in this context. Such models, however, seem appropriate in several small area contexts. For example, the USDA uses satellite data as auxiliary variables in the analysis of many of their crop surveys. Clearly, these measurements are subject to error, and measurement error models seem appropriate in these situations for small area estimation. This article develops a general normal hierarchical Bayesian methodology for small area estimation in such situations. We consider both unit-level and area-specific models and illustrate them in real-life examples as well as with simulated data.

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