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Iterative Multiple Imputation: A Framework to Determine the Number of Imputed Datasets

Author

Listed:
  • Vahid Nassiri
  • Geert Molenberghs
  • Geert Verbeke
  • João Barbosa-Breda

Abstract

We consider multiple imputation as a procedure iterating over a set of imputed datasets. Based on an appropriate stopping rule the number of imputed datasets is determined. Simulations and real-data analyses indicate that the sufficient number of imputed datasets may in some cases be substantially larger than the very small numbers that are usually recommended. For an easier use in various applications, the proposed method is implemented in the R package imi.

Suggested Citation

  • Vahid Nassiri & Geert Molenberghs & Geert Verbeke & João Barbosa-Breda, 2020. "Iterative Multiple Imputation: A Framework to Determine the Number of Imputed Datasets," The American Statistician, Taylor & Francis Journals, vol. 74(2), pages 125-136, April.
    • Handle: RePEc:taf:amstat:v:74:y:2020:i:2:p:125-136
    DOI: 10.1080/00031305.2018.1543615
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