Statistical Science

Multiple Imputation: A Review of Practical and Theoretical Findings

Jared S. Murray

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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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Statist. Sci., Volume 33, Number 2 (2018), 142-159.

First available in Project Euclid: 3 May 2018

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Zentralblatt MATH identifier

Missing data proper imputation congeniality chained equations fully conditional specification sequential regression multivariate imputation


Murray, Jared S. Multiple Imputation: A Review of Practical and Theoretical Findings. Statist. Sci. 33 (2018), no. 2, 142--159. doi:10.1214/18-STS644.

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