Mercer Laina, Wakefield Jon, Chen Cici, Lumley Thomas
Department of Statistics, University of Washington, United States.
Department of Biostatistics, Brown University, United States.
Spat Stat. 2014 May 1;8:69-85. doi: 10.1016/j.spasta.2013.12.001.
Small area estimation (SAE) is an important endeavor in many fields and is used for resource allocation by both public health and government organizations. Often, complex surveys are carried out within areas, in which case it is common for the data to consist only of the response of interest and an associated sampling weight, reflecting the design. While it is appealing to use spatial smoothing models, and many approaches have been suggested for this endeavor, it is rare for spatial models to incorporate the weighting scheme, leaving the analysis potentially subject to bias. To examine the properties of various approaches to estimation we carry out a simulation study, looking at bias due to both non-response and non-random sampling. We also carry out SAE of smoking prevalence in Washington State, at the zip code level, using data from the 2006 Behavioral Risk Factor Surveillance System. The computation times for the methods we compare are short, and all approaches are implemented in R using currently available packages.
小区域估计（SAE）在许多领域都是一项重要工作，被公共卫生和政府组织用于资源分配。通常，会在各个区域内开展复杂调查，在这种情况下，数据通常仅由感兴趣的响应以及反映设计的相关抽样权重组成。虽然使用空间平滑模型很有吸引力，并且已经针对此工作提出了许多方法，但空间模型很少纳入加权方案，这使得分析可能存在偏差。为了检验各种估计方法的特性，我们进行了一项模拟研究，考察由于无应答和非随机抽样导致的偏差。我们还使用2006年行为风险因素监测系统的数据，在邮政编码层面进行了华盛顿州吸烟率的小区域估计。我们比较的方法计算时间较短，并且所有方法都使用当前可用的R包在R中实现。
