2020/06/18 by Jon Wakefield, Wakefield, Jon, Taylor Okonek +3 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Medicine · #Applications (stat.AP) #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Methodology (stat.ME) #Spatial and Panel Data Analysis #demographic modeling and climate adaptation
paper · pdf · doi:10.48550/arxiv.2006.10266
openalex publication_date 2020/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area-level and at the unit-level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015-2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to COVID-19 is discussed.