Multiple Imputation: A Review of Practical and Theoretical Findings
Methodology
2018-01-15 v1
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.
Keywords
Cite
@article{arxiv.1801.04058,
title = {Multiple Imputation: A Review of Practical and Theoretical Findings},
author = {Jared S. Murray},
journal= {arXiv preprint arXiv:1801.04058},
year = {2018}
}