Missing the point: Non-convergence in iterative imputation algorithms


Iterative imputation is a popular tool to accommodate missing data. While it is widely accepted that valid inferences can be obtained with this technique, these inferences all rely on algorithmic convergence. There is no consensus on how to evaluate the convergence properties of the method. Our study provides insight into identifying non-convergence in iterative imputation algorithms. We found that—in the cases considered—inferential validity was achieved after five to ten iterations, much earlier than indicated by diagnostic methods. We conclude that it never hurts to iterate longer, but such calculations hardly bring added value.

First Workshop on the Art of Learning with Missing Values (Artemiss) hosted by the 37 th International Conference on Machine Learning (ICML)
Stef van Buuren
Stef van Buuren

My research interests include data science, missing data, child growth and development, and measurement.