The use of sampling weights in Bayesian hierarchical models for small area estimation.
The use of sampling weights in Bayesian hierarchical models for small area estimation.
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DOI：
10.1016/j.sste.2014.07.002
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发表时间：
2014-10
影响因子：
3.4
通讯作者：
Lumely T
中科院分区：
文献类型：
--
作者：
Chen C;Wakefield J;Lumely T
分层建模已广泛用于小区域估计。然而，这些模型很少考虑反映复杂调查所需的设计权重。我们开发了计算高效的贝叶斯空间平滑模型，该模型承认设计权重。使用集成嵌套拉普拉斯近似进行计算，速度很快。提出了一项模拟研究，考虑了个体的无响应和非随机选择的影响。我们研究了忽略设计权重的影响和空间平滑的好处。结果表明，与标准方法相比，所提出的模型可以大大降低均方误差。通过包含设计权重来减少偏差，并通过分层平滑来减少方差。我们分析了华盛顿州 2006 年行为风险因素监测系统的数据。使用现有软件包，可以轻松快速地将模型安装到 R 环境中。
Hierarchical modeling has been used extensively for small area estimation. However, design weights that are required to reflect complex surveys are rarely considered in these models. We develop computationally efficient, Bayesian spatial smoothing models that acknowledge the design weights. Computation is carried out using the integrated nested Laplace approximation, which is fast. A simulation study is presented that considers the effects of non-response and non-random selection of individuals. We examine the impact of ignoring the design weights and the benefits of spatial smoothing. The results show that, when compared with standard approaches, mean squared error can be greatly reduced with the proposed models. Bias reduction occurs through the inclusion of the design weights, with variance reduction being achieved through hierarchical smoothing. We analyze data from the Washington State 2006 Behavioral Risk Factor Surveillance System. The models are easily and quickly fitted within the R environment, using existing packages.