Multiple Edit/Multiple Imputation for Multivariate Continuous Data
多元连续数据的多重编辑与多重插补AI 解读 ↓
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多元连续数据的多重编辑与多重插补
- 研究问题
- 面对同时包含缺失值和回答误差的数据，如何构造完整且无误差的模拟数据，使推断同时反映两类不确定性？
- 方法与思路
- 作者提出多重编辑与多重插补，模拟多份理想数据后分别分析并按多重插补规则合并，应用于儿童身体测量数据。
- 材料陈述的贡献
- 摘要称该方法统一处理未回答和回答误差，并在所给健康调查应用中发现总体估计对误差率及幅度的先验较不敏感。
- 阅读关注点
- 应核对理想数据分布、未回答机制和误差模型的假设，合并规则的有效性，以及无需追访数据时模型如何获得信息。
- 阅读边界
- 依据版本未注明的 OpenAlex 摘要；具体模型及敏感性范围未展开，个案中的先验不敏感不能推广到其他数据。
原文摘要
Multiple imputation replaces an incomplete dataset with m > 1 simulated complete versions that are analyzed separately by standard methods. We present a natural extension of multiple imputation for handling the dual problems of nonresponse and response error. This extension, which we call multiple edit/multiple imputation (MEMI), replaces an observed dataset containing missing values and errors with m > 1 simulated versions of the ideal dataset that is complete and error-free. These ideal data sets are analyzed separately, and the results are combined using the same rules as for multiple imputation. The resulting inferences simultaneously reflect uncertainty due to nonresponse and response error. MEMI may be an attractive alternative to deterministic or quasi-statistical edit and imputation procedures used by many data-collecting agencies. Producing MEMI's requires assumptions about the distribution of the ideal data, the nature of nonresponse, and a model for the response error mechanism. However, fitting such a model does not necessarily require data from a follow-up study. In this article we develop and implement MEMI for preliminary data from the Third National Health and Nutrition Examination Survey, Phase I (1988–1991). Raw body measurements for 1,345 children age 2–3 years are imputed under a Bayesian model for intermittent or semicontinuous errors. The resulting population estimates are found to be quite insensitive to prior assumptions about the rates and magnitude of errors.