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，有效的推断基于不完全观察到的数据集是可能的，如果丢失的数据问题得到妥善处理。数据库制作者支持的一种日益被接受的弥补缺失数据的方法是多重估算法。现有的基于模型的多重插补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，大多数可用的软件不是设计来有效地处理大型复杂的集群或面板数据集。在本项目中，将扩展多重插补程序，以便根据近似贝叶斯方法对复杂数据集进行有效和稳健的插补，从而进行有效和更精确的推断。将根据现有软件和有待开发的功能和模块（可在R中调用），特别是关于文献中讨论的可能局限性，出版多重插补方法使用指南。将通过实质性应用和通过对真实的数据集的分析来说明需要开发这些扩展。将向科学界提供估算程序。

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.