Abstract
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 language | English |
|---|---|
| Pages (from-to) | 693-720 |
| Number of pages | 28 |
| Journal | Japanese Journal of Statistics and Data Science |
| Volume | 3 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2020 Dec |
| Externally published | Yes |
Keywords
- 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