Small Area Estimation‐New Developments and Directions

By Danny Pfeffermann. 2002

326 citations recorded by the source corpus.

The purpose of this paper is to provide a critical review of the main advances in small area estimation (SAE) methods in recent years. We also discuss some of the earlier developments, which serve as a necessary background for the new studies. The review focuses on model dependent methods with special emphasis on point prediction of the target area quantities, and mean square error assessments. The new models considered are models used for discrete measurements, time series models and models that arise under informative sampling. The possible gains from modeling the correlations among small area random effects used to represent the unexplained variation of the small area target quantities are examined. For review and appraisal of the earlier methods used for SAE, see Ghosh & Rao (1994).

Subfields: Economics and Econometrics, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ef3457d11123d3bc078726b029598f2644c2922e

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Citations in the corpus

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).”
Model Selection by Testing for the Presence of Small-Area Effects, and Application to Area-Level Data (2011, unknown): “2003, 2006), Pfeffermann (2002), Jiang and Lahiri (2006), and Datta (2009).”
Small-Area Estimation With State–Space Models Subject to Benchmark Constraints (2006, methodology): “(For a recent review of small-area estimation methods, see Pfeffermann 2002.”
Multinomial-based small area estimation of labour force indicators* (2013, unknown): “See a description of this theory in the monograph of Rao (2003), or in the reviews of Ghosh and Rao (1994), Rao (1999), Pfeffermann (2002) and Jiang and Lahiri (2006).”
Bootstrap Approximation to Prediction MSE for State–Space Models with Estimated Parameters (2005, background): “A similar problem arising in Small Area estimation based on cross-sectional models evoked extensive research in the last two decades on plausible bias corrections (see Pfeffermann, 2002 for a recent review).”
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.”
Small area estimation in the presence of correlated random area effects (2009, background): “Firstly, we analyse the extent to which the spatial effects in data used for SAE justify the introduction of an autocorrelation parameter in the Fay–Herriot model.”
New important developments in small area estimation (2013, methodology): “Pfeffermann and Sverchkov (2007) illustrate their approach by assuming that the sample model is the unit-level model (5.”
Mixed model prediction and small area estimation (2006, background): “See Pfeffermann (2002) and Rao (2003) for review of time-series, cross-sectional models that have been used for SAE.”
Small Area Shrinkage Estimation (2012, methodology): “For a detailed exposure to small area estimation, the reader is referred to the recent book of Rao (2003a) and the review articles of Ghosh and Rao (1994), Pfeffermann (2002), Rao (1999, 2003b) and Datta (2009).”
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.”