The use of multiple imputation for the analysis of missing data
The use of multiple imputation for the analysis of missing data
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DOI：
10.1037//1082-989x.6.4.317
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发表时间：
2001-12-01
影响因子：
7
通讯作者：
Russell, D
中科院分区：
文献类型：
--
作者：
Sinharay, S;Stern, HS;Russell, D
本文对多重插补（MI）进行了全面的综述，MI是一种分析缺失值数据集的技术。形式上，MI是用一组m > 1个合理值替换每个缺失数据点以生成m个完整数据集的过程。这些完整的数据集然后通过标准统计软件进行分析，并将结果合并，以给出参数估计值和标准误差，其中考虑了由于缺失数据值而导致的不确定性。本文介绍了MI背后的想法，讨论了MI的优势，现有的技术解决缺失数据，介绍了如何做MI的真实的问题，审查可实现MI的软件，并讨论了模拟研究的结果，旨在找出如何假设的插补模型影响MI提供的参数估计。
This article provides a comprehensive review of multiple imputation (MI), a technique for analyzing data sets with missing values. Formally, MI is the process of replacing each missing data point with a set of m > 1 plausible values to generate m complete data sets. These complete data sets are then analyzed by standard statistical software, and the results combined, to give parameter estimates and standard errors that take into account the uncertainty due to the missing data values. This article introduces the idea behind MI, discusses the advantages of MI over existing techniques for addressing missing data, describes how to do MI for real problems, reviews the software available to implement MI, and discusses the results of a simulation study aimed at finding out how assumptions regarding the imputation model affect the parameter estimates provided by MI.