Multiple Imputation: A Review of Practical and Theoretical Findings

• Autores: Jared S. Murray
• Localización: Statistical science, ISSN 0883-4237, Vol. 33, Nº. 2, 2018, págs. 142-159
• Idioma: inglés
• DOI: 10.1214/18-sts644
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• Resumen

  • 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.