Multiple Imputation: A Review of Practical and Theoretical Findings

Jared S. Murray

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arXiv · 1801.04058

Multiple Imputation: A Review of Practical and Theoretical Findings

Jared S. Murray

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Abstract

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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Jared S. Murray. 2018-01-12. Multiple Imputation: A Review of Practical and Theoretical Findings. https://arxiv.org/abs/1801.04058

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