Robust and efficient multiple imputation of complex data sets

即使在精心进行的科学调查中也会出现数据缺失的情况。然而，基于不完全观察到的数据集的有效推断只有在丢失数据问题得到适当处理的情况下才有可能。一种越来越被数据库生产者所接受的补偿缺失数据的方法是多重插入法。现有的基于模型的生成多重输入的技术仅限于全参数模型，如果指定不当，可能会产生不必要的不精确甚至有偏差的推断。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。在本项目中，将扩展多个输入程序，以便基于近似贝叶斯方法对复杂数据集进行有效和稳健的输入，从而允许有效和更精确的推断。将出版根据现有软件和将要开发的功能和模块（可用R语言调用）使用多重输入方法的准则，特别是关于文献中讨论的可能的限制。将通过实质性应用和对实际数据集的分析来说明需要发展的扩展。计算程序将提供给科学界。

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 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 to be developed will be illustrated through substantive applications and through analyses of real data sets. The imputation programs will be made available to the scientific community.