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.
翻译:迭代估算法是容纳缺失数据的一种常用工具。 虽然人们广泛认为可以用这种技术获得有效的推论,但这些推论都依赖于算法趋同。 对于如何评价该方法的趋同特性没有达成共识。 我们的研究为确定迭代估算法中的非趋同性提供了深刻的见解。 我们发现,在五至十次反复之后,被认为的推论有效性就实现了,远远早于诊断方法所显示的。 我们的结论是,这种推论永远不会伤害它的时间更长,但这种计算几乎没有带来附加值。