Missing data: Our view of the state of the art
Missing data: Our view of the state of the art
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DOI：
10.1037//1082-989x.7.2.147
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发表时间：
2002-06-01
影响因子：
7
通讯作者：
Graham, JW
中科院分区：
文献类型：
--
作者：
Schafer, JL;Graham, JW
缺失数据的统计程序已大大改进，但误解和不健全的做法仍然比比皆是。作者提出了缺失数据问题，回顾了方法，提供了建议，并提出了尚未解决的问题。他们澄清了关于随机缺失（MAR）概念的常见误解。他们总结了反对旧程序的证据，除了少数例外，不鼓励使用它们。他们以技术和实用的语言提出了两种强烈推荐的一般方法：最大似然（ML）和贝叶斯多重插补（MI）。更新的发展进行了讨论，包括一些处理缺失的数据，不是MAR。虽然还没有成为主流，这些程序可能最终扩展ML和MI方法，目前代表的最先进的。
Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.