Small area estimation with mixed models: a review

Shonosuke Sugasawa, Tatsuya Kubokawa

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Small area estimation is recognized as an important tool for producing reliable estimates under limited sample information. This paper reviews techniques of small area estimation using mixed models, covering from basic to recently proposed advanced ones. We first introduce basic mixed models for small area estimation, and provide several methods for computing mean squared errors and confidence intervals which are important for measuring uncertainty of small area estimators. Then we provide reviews of recent development and techniques in small area estimation. This paper could be useful not only for researchers who are interested in details on the methodological research in small area estimation, but also for practitioners who might be interested in the application of the basic and new methods.

Original languageEnglish
Pages (from-to)693-720
Number of pages28
JournalJapanese Journal of Statistics and Data Science
Issue number2
Publication statusPublished - 2020 Dec
Externally publishedYes


  • Best linear unbiased predictor
  • Empirical Bayes
  • Fay–Herriot model
  • Hierarchical Bayes
  • Linear mixed model
  • Maximum likelihood estimator
  • Mean squared error
  • Nested error regression model
  • Shrinkage

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Theory and Mathematics


Dive into the research topics of 'Small area estimation with mixed models: a review'. Together they form a unique fingerprint.

Cite this