Missing the Point: Non-Convergence in Iterative Imputation Algorithms
Abstract
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.
Cite
Text
Oberman et al. "Missing the Point: Non-Convergence in Iterative Imputation Algorithms." ICML 2020 Workshops: Artemiss, 2020.Markdown
[Oberman et al. "Missing the Point: Non-Convergence in Iterative Imputation Algorithms." ICML 2020 Workshops: Artemiss, 2020.](https://mlanthology.org/icmlw/2020/oberman2020icmlw-missing/)BibTeX
@inproceedings{oberman2020icmlw-missing,
title = {{Missing the Point: Non-Convergence in Iterative Imputation Algorithms}},
author = {Oberman, Hanne I. and van Buuren, Stef and Vink, Gerko},
booktitle = {ICML 2020 Workshops: Artemiss},
year = {2020},
url = {https://mlanthology.org/icmlw/2020/oberman2020icmlw-missing/}
}