Small area estimation - New developments and directions

Danny Pfeffermann*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

187 Scopus citations

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).

Original languageEnglish
Pages (from-to)125-143
Number of pages19
JournalInternational Statistical Review
Volume70
Issue number1
DOIs
StatePublished - Apr 2002

Keywords

  • Best linear unbiased prediction
  • Cross-sectional correlations
  • Empirical Bayes
  • Hierarchical Bayes
  • Informative sampling
  • Mixed models
  • Time series models

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