标题： 如何对插补方法进行排序？ 显示英文标题

标题： How to rank imputation methods?

摘要： 填补是一种处理缺失值广泛问题的有吸引力的工具。 因此，在过去十年中，研究和开发填补方法一直是活跃的研究领域。 面对一个填补任务和大量方法，如何找到最合适的填补方法？ 尽管在不同情境下的模型选择，如预测，已经得到了很好的研究，但这个问题似乎没有受到太多关注。 在本文中，我们遵循填补评分（I-评分）的概念，并开发了一个新的、可靠且易于实现的评分，用于在不访问完整数据的情况下对给定数据集的缺失值填补进行排序。 在实践中，通常是通过人为地掩盖观测值，使用诸如均方根误差（RMSE）等度量来比较填补值与观测值。 我们讨论了如果不够谨慎，这种额外掩盖观测值的方法可能会产生误导，并且在MAR条件下通常无效。 然后，我们确定了一个新的缺失性假设，并开发了一个评分，该评分结合了合理的观测值掩盖与适当的评分规则。 因此，排序旨在找到最能复制数据分布的填补方法，从而找到适用于一系列下游任务的填补方法。 我们展示了该评分的适当性，并讨论了一个涉及能量评分的估计算法。 最后，我们在模拟数据示例以及一个下游任务中展示了新评分的有效性。 显示英文摘要

摘要： Imputation is an attractive tool for dealing with the widespread issue of missing values. Consequently, studying and developing imputation methods has been an active field of research over the last decade. Faced with an imputation task and a large number of methods, how does one find the most suitable imputation? Although model selection in different contexts, such as prediction, has been well studied, this question appears not to have received much attention. In this paper, we follow the concept of Imputation Scores (I-Scores) and develop a new, reliable, and easy-to-implement score to rank missing value imputations for a given data set without access to the complete data. In practice, this is usually done by artificially masking observations to compare imputed to observed values using measures such as the Root Mean Squared Error (RMSE). We discuss how this approach of additionally masking observations can be misleading if not done carefully and that it is generally not valid under MAR. We then identify a new missingness assumption and develop a score that combines a sensible masking of observations with proper scoring rules. As such the ranking is geared towards the imputation that best replicates the distribution of the data, allowing to find imputations that are suitable for a range of downstream tasks. We show the propriety of the score and discuss an estimation algorithm involving energy scores. Finally, we show the efficacy of the new score in simulated data examples, as well as a downstream task.