Accurate Confidence Interval Estimation of Small Area Parameters Under the Fay–Herriot Model |
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Authors: | Lixia Diao David D Smith Gauri Sankar Datta Tapabrata Maiti Jean D Opsomer |
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Institution: | 1. M.D. Anderson Cancer Center;2. Department of Mathematics, Tennessee Technological University;3. Department of Statistics, University of Georgia;4. Department of Statistics, Michigan State University;5. Department of Statistics, Colorado State University |
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Abstract: | Small area estimation has long been a popular and important research topic due to its growing demand in public and private sectors. We consider here the basic area level model, popularly known as the Fay–Herriot model. Although much of current research is predominantly focused on second order unbiased estimation of mean squared prediction errors, we concentrate on developing confidence intervals (CIs) for the small area means that are second order correct. The corrected CI can be readily implemented, because it only requires quantities that are already estimated as part of the mean squared error estimation. We extend the approach to a CI for the difference of two small area means. The findings are illustrated with a simulation study. |
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Keywords: | calibration confidence intervals estimated best linear unbiased predictor Fay– Herriot model small area estimation |
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