Robust small area estimation: An overview

Jiming Jiang, J. Sunil Rao

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations


A small area typically refers to a subpopulation or domain of interest for which a reliable direct estimate, based only on the domain-specific sample, cannot be produced due to small sample size in the domain. While traditional small area methods and models are widely used nowadays, there have also been much work and interest in robust statistical inference for small area estimation (SAE). We survey this work and provide a comprehensive review here. We begin with a brief review of the traditional SAE methods. We then discuss SAE methods that are developed under weaker assumptions and SAE methods that are robust in certain ways, such as in terms of outliers or model failure. Our discussion also includes topics such as nonparametric SAE methods, Bayesian approaches, model selection and diagnostics, and missing data. A brief review of software packages available for implementing robust SAE methods is also given.

Original languageEnglish (US)
Pages (from-to)337-360
Number of pages24
JournalAnnual Review of Statistics and Its Application
StatePublished - Mar 7 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 Annual Review of Statistics and Its Application. All rights reserved.


  • borrowing strength
  • empirical best linear unbiased prediction
  • empirical best prediction,method of moments,model failure
  • mean squared prediction error
  • model misspecification
  • model selection
  • nonparametric
  • observed best prediction
  • outliers
  • robustness
  • small area estimation


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