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May 2018 Multiple Imputation: A Review of Practical and Theoretical Findings
Jared S. Murray
Statist. Sci. 33(2): 142-159 (May 2018). DOI: 10.1214/18-STS644

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. "Multiple Imputation: A Review of Practical and Theoretical Findings." Statist. Sci. 33 (2) 142 - 159, May 2018. https://doi.org/10.1214/18-STS644

Information

Published: May 2018
First available in Project Euclid: 3 May 2018

zbMATH: 1397.62052
MathSciNet: MR3797707
Digital Object Identifier: 10.1214/18-STS644

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.33 • No. 2 • May 2018
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