Multiple Imputation: Research for the Third Decade

多重插补：第三个十年的研究

• 批准号: 9705158
• 负责人: Donald Rubin
• 金额: $ 24.05万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-08-01 至 2000-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9705158&HistoricalAwards=false
• 关键词: Multiple; Imputation; Research; Third; Decade

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.

多重插补：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 多重插补的第三个十年始于不断增长的 应用程序，甚至进入最初没有提出的领域（例如，环境研究，化学），越来越多的 软件的可用性，以及越来越多的统计 正在进行的研究工作。 扩展的，几乎是常规的，使用 多重估算意味着许多技术问题， 在其早期发展中被认为是相对次要的，需要 关注 这项研究解决了几个最重要的 问题，特别是：（1）根据 更通用和灵活的模型;（2）创建“嵌套”多个 大部分和高度可变的数据集的估算 （3）使用交叉匹配技术， 分析只有少数几个多重插补的数据集， 缺失信息的分数，特别是为了获得有效的 p值;（4）分析具有嵌套多重的数据集 估算;和（5）进行探索和诊断 多重插补数据集的分析。 缺失值在许多数据集中普遍存在，并且可能是进行推断的巨大障碍。 多重插补具有 被证明是一个有用的推理模式，在存在失踪 数据 多重插补的基本目的是允许使用者 不完整的数据集，他们通常对 缺失数据机制，以达到有效的统计推断 仅使用（1）标准完整数据分析技术和（2） 组合完整数据输出的简单规则 分析。 多重填补已成功地用于许多 包括在社会和经济科学领域， 科学史和生物医学应用。 这一努力 扩展了多个应用的适用性和易用性， 估算，进入第三个十年，它应该成为 这是日常统计实践中的一个重要工具。