New important developments in small area estimation

By Danny Pfeffermann. 2013

437 citations recorded by the source corpus.

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 (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.

Subfields: Economics and Econometrics, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/df3858fc0d80ed30662e609fb0d0555a69161c10

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Citations in the corpus

From start to finish: a framework for the production of small area official statistics (2018, methodology): “Benchmarked small area estimates offer an attractive property for NSIs (see Ghosh and Steorts (2013), Pfeffermann (2013) and Pfeffermann et al. (2014) for a discussion on benchmarking methods).”
Empirical Best Prediction Under Unit-Level Logit Mixed Models (2016, background): “See the monographs of Rao (2003) and Rao and Molina (2015), and the reviews of Ghosh and Rao (1994), Rao (1999), Pfeffermann (2002, 2013), and Jiang and Lahiri (2006) for an introduction to SAE.”
Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations (2017, background): “Pfeffermann (2013) provides further general discussion on the shortcomings of design-based estimation strategies concerning small-area estimation.”
Small area estimation of labour force indicators under a multinomial model with correlated time and area effects (2015, background): “Some descriptions of small area estimation theory can be found in Rao (2003), or in Ghosh and Rao (1994), Rao (1999), Pfeffermann (2002, 2013) and Jiang and Lahiri (2006).”
Small area estimation of general parameters with application to poverty indicators: A hierarchical Bayes approach (2014, methodology): “The monograph of Rao (2003) contains a comprehensive account of small area estimation techniques that appeared until the publication date; see Jiang and Lahiri (2006), Datta (2009) and Pfeffermann (2013) for reviews of the more recent work.”
Empirical best prediction under area-level Poisson mixed models (2016, background): “See the monograph Rao (2003) and the reviews of Ghosh and Rao (1994), Rao (1999), Pfeffermann (2002), Jiang and Lahiri (2006), Rao (2008) and Pfeffermann (2013) for an introduction to the SAE.”
Bayesian Estimators for Small Area Models when Auxiliary Information is Measured with Error (2015, background): “Consequently, model-based estimation of small area means has been receiving a lot of attention in statistical literature where the goal is to extract information from other sources; see Rao (2003), Datta (2009) and Pfefferman (2013) for a complete review.”