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）是许多领域的一项重要工作，被公共卫生和政府组织用于资源分配。通常，复杂的调查是在区域内进行的，在这种情况下，数据通常仅包含感兴趣的响应和反映设计的相关抽样权重。虽然使用空间平滑模型很有吸引力，并且为此提出了许多方法，但空间模型很少结合加权方案，从而使分析可能存在偏差。为了检查各种估计方法的特性，我们进行了模拟研究，观察由于无响应和非随机抽样造成的偏差。我们还利用 2006 年行为风险因素监测系统的数据，对华盛顿州的邮政编码层面的吸烟率进行了 SAE。我们比较的方法的计算时间很短，并且所有方法都是使用当前可用的包在R中实现的。