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Robust and efficient multiple imputation of complex data sets

复杂数据集的稳健且高效的多重插补

基本信息

批准号：
162411054
负责人：
Professor Dr. Jost Reinecke
金额：
--
依托单位：
Arbeitsbereich Psychologische Methoden und Statistik
依托单位国家：
德国
项目类别：
Priority Programmes
财政年份：
2010
资助国家：
德国
起止时间：
2009-12-31 至 2011-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/162411054?language=en
关键词：
Robust efficient multiple imputation complex

项目摘要

Missing data occur even in carefully conducted scientific surveys. However, valid inferences based on incompletely observed data sets are only possible if the missing data problem is handled properly. One increasingly accepted method supported by data base producers to compensate for missing data is the method of multiple imputation. Available model-based techniques of generating multiple imputations are restricted to fully parametric models, which, if misspecified may produce unnecessarily imprecise or even biased inferences. Furthermore, most of the available software is not designed to efficiently handle large complex clustered or panel data sets. In this project, multiple imputation procedures will be extended to enable efficient and robust imputation of complex data sets based on an approximate Bayesian and a Bayesian approach, thus allowing valid and more precise inferences. Guidelines for the use of the multiple imputation method, based on currently available software and on functions and modules to be developed (callable in R), will be published, particularly with regard to possible limitations discussed in the literature. The need of the extensions developed will be illustrated based on substantive applications and through analyses of real data sets. The imputation programs will be made available to the scientific community.

即使在仔细进行的科学调查中，也会出现数据缺失的情况。然而，只有在正确处理缺失数据问题的情况下，才可能基于不完全观察到的数据集进行有效的推断。数据库生产者支持的一种日益被接受的弥补缺失数据的方法是多重补偿方法。现有的基于模型的生成多个推论的技术仅限于完全参数模型，如果错误指定，可能会产生不必要的不精确甚至有偏见的推断。此外，大多数可用的软件都不能有效地处理大型、复杂的集群或面板数据集。在该项目中，将扩展多个推算程序，以基于近似贝叶斯和贝叶斯方法对复杂数据集进行高效和稳健的推算，从而能够进行有效和更精确的推断。将根据目前可用的软件和待开发的函数和模块(在R中可调用)发布使用多重归因法的指导方针，特别是关于文献中讨论的可能的限制。将根据实质性应用程序并通过对实际数据集的分析来说明所开发扩展的必要性。这些归责计划将向科学界提供。