Wakefield Jon, Okonek Taylor, Pedersen Jon
Department of Biostatistics, University of Washington, Seattle, USA.
Department of Statistics, University of Washington, Seattle, USA.
Int Stat Rev. 2020 Aug;88(2):398-418. doi: 10.1111/insr.12400. Epub 2020 Jul 24.
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.
小区域估计（SAE）需要对通常为地理区域的领域的感兴趣特征进行估计，在这些领域中可能几乎没有或根本没有可用样本。SAE有着悠久的历史，并且从各种各样令人困惑的哲学观点出发，人们提出了各种各样的方法。我们描述了基于设计和基于模型的方法以及在区域层面和单位层面指定的模型，重点是健康应用和全贝叶斯空间模型。当响应数据稀疏时，辅助信息的使用是成功进行推断的关键因素，我们讨论了一些允许纳入协变量数据的方法。利用2015 - 2016年在马拉维进行的人口健康调查收集的数据对艾滋病毒流行率进行小区域估计，用于说明一些技术。还讨论了小区域估计技术在与2019冠状病毒病相关结果方面的潜在用途。
