Small Area Estimation-New Developments and Directions |
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Authors: | Danny Pfeffermann |
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Affiliation: | Department of Statistics, Hebrew University, Jerusalem, Israel and Department of Social Statistics, University of Southampton, UK |
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Abstract: | The purpose of this paper is to provide a critical review of the main advances in small area estimation (SAE) methods in recent years. We also discuss some of the earlier developments, which serve as a necessary background for the new studies. The review focuses on model dependent methods with special emphasis on point prediction of the target area quantities, and mean square error assessments. The new models considered are models used for discrete measurements, time series models and models that arise under informative sampling. The possible gains from modeling the correlations among small area random effects used to represent the unexplained variation of the small area target quantities are examined. For review and appraisal of the earlier methods used for SAE, see Ghosh & Rao (1994). |
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Keywords: | Best linear unbiased prediction Cross-sectional correlations Empirical Bayes Hierarchical Bayes Informative sampling Mixed models Time series models |
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