Multiple Imputation: Research for the Third Decade

多重归因：第三个十年的研究DMS-9705158唐纳德·B·鲁宾和约翰·巴纳德哈佛大学多重归因的第三个十年始于不断增长的应用程序，甚至进入了最初没有提出的领域(例如，环境研究、化学)，软件的可用性越来越高，正在进行的统计研究工作也越来越多。扩展的、几乎是例行公事的多重归罪的使用意味着许多技术问题需要关注，这些问题在早期的开发中被认为是相对次要的。这项研究解决了几个最重要的问题，特别是：(1)在更通用和更灵活的模型下创建多个推算；(2)在具有大且高度可变的缺失信息的数据集中创建“嵌套”的多个推算；(3)使用交叉匹配技术来分析只有几个多推算但大部分信息缺失的数据集，特别是为了获得有效的p值；(4)分析具有嵌套的多推算的数据集；以及(5)对多推算的数据集进行探索性和诊断性分析。缺失值在许多数据集中很普遍，可能会对进行推理造成很大的阻碍。事实证明，在存在缺失数据的情况下，多重推算是一种有用的推理模式。多重推算的基本目的是允许不完整数据集的用户仅使用(1)标准完整数据分析技术和(2)组合完整数据分析输出的简单规则来得出有效的统计推断，这些用户通常对缺失数据机制知之甚少。多重归因已被成功地用于许多背景，包括在社会科学和经济科学、科学史和生物医学应用中。这一努力扩大了多重推算的适用性和易用性。多重推算已进入第三个十年，应当成为日常统计实践中的一个重要工具。

Multiple Imputation: Research for the Third Decade DMS-9705158 Donald B. Rubin and John Barnard Harvard University The third decade of multiple imputation begins with ever-growing applications, even into areas not originally proposed (e.g., environmental studies, chemistry), an increasing availability of software, and an increasing amount of statistical research work being conducted. The expanded, almost routine, use of multiple imputation means that many technical issues, considered relatively minor in its early development, need attention. This research addresses several of the most important issues, in particular: (1) creating multiple imputations under more general and flexible models; (2) creating "nested" multiple imputations in data sets with large and highly variable fractions of missing information; (3) using cross-match techniques to analyze data sets having only a few multiple imputations but large fractions of missing information, especially for obtaining valid p-values; (4) analyzing data sets with nested multiple imputations; and (5) conducting exploratory and diagnostic analyses on multiply-imputed data sets. Missing values are prevalent in many data sets and can be a great hindrance in making inference. Multiple imputation has proven to be a useful mode of inference in the presence of missing data. The basic aim of multiple imputation is to allow users of incomplete data sets, who typically have little information about the missing-data mechanism, to reach valid statistical inferences using only (1) standard complete-data analysis techniques and (2) simple rules for combining the output of the complete-data analyses. Multiple imputation has been successfully used in many contexts including in the social and economic sciences, the history of science, and in biomedical applications. This effort extends the applicability and the ease of use of multiple imputation, which enters its third decade where it should become an important tool in everyday statistical practice.