Struggles with survey weighting and regression modeling
Struggles with survey weighting and regression modeling
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DOI：
10.1214/088342306000000691
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发表时间：
2007-05-01
影响因子：
5.7
通讯作者：
Gelman, Andrew
中科院分区：
文献类型：
--
作者：
Gelman, Andrew
贝叶斯数据分析的一般原则意味着，调查答复模型的构建应以影响纳入和不答复概率的所有变量为条件，这些变量也是调查加权和聚类中使用的变量。然而，这种模型可能很快变得非常复杂，可能有数千个后分层细胞。然后，它是一个挑战，开发一般家庭的多层次概率模型，产生合理的贝叶斯推理。我们在几个正在进行的公共卫生和社会调查的背景下进行讨论。这项工作目前是开放式的，我们的结论是如何研究可以继续解决这些问题的想法。
The general principles of Bayesian data analysis imply that models for survey responses should be constructed conditional on all variables that affect the probability of inclusion and nonresponse, which are also the variables used in survey weighting and clustering. However, such models can quickly become very complicated, with potentially thousands of poststratification cells. It is then a challenge to develop general families of multilevel probability models that yield reasonable Bayesian inferences. We discuss in the context of several ongoing public health and social surveys. This work is currently open-ended, and we conclude with thoughts on how research could proceed to solve these problems.