Small area estimation: new developments and directions
Small area estimation: new developments and directions
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 and Rao (1994).
best linear unbiased prediction, cross-sectional correlations, empirical bayes, hierarchical bayes, informative sampling, mixed models, time series models
125-143
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
2002
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
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 and Rao (1994).
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Published date: 2002
Keywords:
best linear unbiased prediction, cross-sectional correlations, empirical bayes, hierarchical bayes, informative sampling, mixed models, time series models
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Local EPrints ID: 38494
URI: http://eprints.soton.ac.uk/id/eprint/38494
ISSN: 0306-7734
PURE UUID: 7632891d-1908-4686-b43d-fced2f063c4c
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Date deposited: 19 Jun 2006
Last modified: 15 Mar 2024 08:08
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