# New Important Developments in Small Area Estimation

arXiv: https://arxiv.org/abs/1302.4907 · HTML: https://mlsanity.com/paper/1302.4907/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2013.

## Built on by (most cited first, 3 of 3)

- [A step towards the integration of machine learning and classic model-based survey methods](https://mlsanity.com/paper/2402.07521/) (2024)
- [Small Area Estimation with Random Forests and the LASSO](https://mlsanity.com/paper/2308.15180/) (2023)
- [Geographically Weighted Surrogate Models for Rapid Small-Area Chronic Disease Estimation](https://mlsanity.com/paper/2607.28655/) (2026)

## Abstract (arXiv)

The problem of small area estimation (SAE) is how to produce reliable estimates of characteristics of interest such as means, counts, quantiles, etc., for areas or domains for which only small samples or no samples are available, and how to assess their precision. The purpose of this paper is to review and discuss some of the new important developments in small area estimation methods. Rao [Small Area Estimation (2003)] wrote a very comprehensive book, which covers all the main developments in this topic until that time. A few review papers have been written after 2003, but they are limited in scope. Hence, the focus of this review is on new developments in the last 7-8 years, but to make the review more self-contained, I also mention shortly some of the older developments. The review covers both design-based and model-dependent methods, with the latter methods further classified into frequentist and Bayesian methods. The style of the paper is similar to the style of my previous review on SAE published in 2002, explaining the new problems investigated and describing the proposed solutions, but without dwelling on theoretical details, which can be found in the original articles. I hope that this paper will be useful both to researchers who like to learn more on the research carried out in SAE and to practitioners who might be interested in the application of the new methods.