收入的两阶段数据合成：在NHIS中的应用
Two-Phase Data Synthesis for Income: An Application to the NHIS
摘要：
我们提出了一个两阶段的合成过程，合成收入，一个敏感的变量，通常是高度倾斜，并有一些报告的零。我们考虑两种形式的连续收入变量：二元形式，在第1阶段建模和合成;非负连续形式，在第2阶段建模和合成。贝叶斯综合模型提出的两阶段的合成过程中，和其他合成模型是容易实现的。我们展示了我们的方法与应用程序的样本从全国健康访谈调查（NHIS）。所生成的合成数据集的效用和风险概况进行了评估，并与单相合成过程的结果进行了比较。
英文摘要：
We propose a two-phase synthesis process for synthesizing income, a sensitive variable which is usually highly-skewed and has a number of reported zeros. We consider two forms of a continuous income variable: a binary form, which is modeled and synthesized in phase 1; and a non-negative continuous form, which is modeled and synthesized in phase 2. Bayesian synthesis models are proposed for the two-phase synthesis process, and other synthesis models are readily implementable. We demonstrate our methods with applications to a sample from the National Health Interview Survey (NHIS). Utility and risk profiles of generated synthetic datasets are evaluated and compared to results from a single-phase synthesis process.
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