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Bayesian Benchmarking with Applications to Small Area Estimation
Bayesian Benchmarking with Applications to Small Area Estimation
RRS2009-01
Abstract
It is well-known that small area estimation needs explicit, or at least implicit use of models. These model-based estimates can differ widely from the direct estimates, especially for areas with very low sample sizes. While model-based small area estimates are very useful, one potential difficulty with such estimates is that when aggregated, the overall estimate for a larger geographical area may be quite different from the corresponding direct estimate, the latter being usually believed to be quite reliable. This is because the original survey was designed to achieve specified inferential accuracy at this higher level of aggregation. The problem can be more severe in the event of model failure as often there is no real check for validity of the assumed model. Moreover, an overall agreement with the direct estimates at an aggregate level may sometimes be necessary for policy reasons to convince the legislators of the utility of small area estimates.
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Related Information
WORKING PAPER
Statistical Research Reports and Studies
Page Last Revised - October 28, 2021
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