Abstract. Small area estimation was discussed in this section of The Survey Statistician in the July 2010 issue by Danny Pfeffermann in his article "Small Area Estimation: Basic Concepts, Models and Ongoing Research". A more comprehensive review article was published a couple years later (Pfeffermann 2013). The Wiley book "Small Area Estimation, Second Edition" of 2015 by J.N.K. Rao and Isabel Molina presents an update to Rao's monograph of 2003 on small area estimation. These important sources cover model-based and design-based approaches on small area estimation (SAE) and show in particular the progress in model-based methods, and the progress is ongoing. In this article we introduce methods that incorporate assisting models in a design-based estimation procedure for population characteristics (totals, means etc.) for subgroups or domains, including small domains (with small sample size). We use logistic mixed models in model-assisted calibration estimation of poverty rates for administrative regions (domains of interest). Statistical properties (design bias and accuracy) of the method is compared with the classical model-free calibration method of Deville and Särndal (1992) and further, with a model-based SAE method that relies on the same logistic mixed model as the model-assisted counterpart. Our design-based simulation experiments employ real data obtained from registers of Statistics Finland. The paper is partly based on Lehtonen and Veijanen (2018).
|Tidskrift||The Survey Statistician|
|Status||Publicerad - jan. 2019|
|MoE-publikationstyp||B1 Artikel i en vetenskaplig tidskrift|
- 112 Statistik