Extending and Implementing Multiple Imputation Technology

扩展和实施多重插补技术

• 批准号: 8805433
• 负责人: Donald Rubin
• 金额: $ 16.54万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-07-01 至 1992-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8805433&HistoricalAwards=false
• 关键词: Extending; Implementing; Multiple; Imputation; Technology

Multiple imputation technology refers to a family of techniques for dealing with incomplete data. This innovative technology, to be perfected under the recommended accomplishment- based renewal, will be especially useful in the context of data bases that have been constructed at great public expense and are to be made available to many data analysts with their arsenals of standard statistical software. Therefore, and in addition to its importance for the academic research community, this innovative technology has implications for statistical endeavors undertaken by government, such as census undercount adjustment and similar forms of compensation for deficient data (so-called "statistical repair work"). Such work is needed more than ever before as data collection resources shrink and missing data problems multiply owing to financial and other restrictions. Good quality analysis is also at stake when, as is becoming increasingly true, incompleteness pervades intensively used scientific data from sources as far-ranging as social surveys and clinical medical protocols.

多重填补技术是指一系列处理不完整数据的技术。这项创新技术将在建议的以成绩为基础的更新办法下加以完善，在以巨额公共费用建立的数据库中特别有用，许多数据分析员可以使用他们的标准统计软件。因此，除了对学术研究界的重要性之外，这项创新技术还对政府开展的统计工作有影响，例如人口普查不足的调整和对有缺陷的数据的类似形式的补偿(所谓的“统计修复工作”)。由于资金和其他方面的限制，数据收集资源缩水，丢失数据的问题成倍增加，因此比以往任何时候都更需要这样的工作。当大量使用的科学数据从社会调查和临床医学方案中广泛使用时，高质量的分析也岌岌可危，因为这一点正变得越来越真实。