期刊
STATISTICAL SCIENCE
卷 33, 期 2, 页码 142-159出版社
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/18-STS644
关键词
Missing data; proper imputation; congeniality; chained equations; fully conditional specification; sequential regression multivariate imputation
资金
- National Science Foundation [SES-1130706, SES-1631970, DMS-1043903]
Multiple imputation is a straightforward method for handling missing data in a principled fashion. This paper presents an overview of multiple imputation, including important theoretical results and their practical implications for generating and using multiple imputations. A review of strategies for generating imputations follows, including recent developments in flexible joint modeling and sequential regression/chained equations/fully conditional specification approaches. Finally, we compare and contrast different methods for generating imputations on a range of criteria before identifying promising avenues for future research.
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