分布鲁棒且可泛化的推断
Distributionally robust and generalizable inference
AI总结：
该研究针对实际数据非独立同分布导致的统计推断不可靠问题，综述了两种基于单数据集的分布稳定性量化方法：最坏情况扰动敏感度分析和随机偏移下的平均鲁棒性评估，后者还能整合两类不确定性得到统一置信区间，助力提升统计发现的泛化性。
摘要：
我们探讨了近期开发的、用于量化统计发现在分布变化下的稳定性与可泛化性的方法。在许多实际问题中，数据并非从目标总体中独立同分布（i.i.d.）抽取。例如，未观测到的抽样偏差、批次效应或未知关联可能会使方差比独立同分布抽样时更大。因此，为实现可靠的统计推断，有必要考虑这类变异。我们讨论并综述了两种可基于单个数据集量化分布稳定性的方法。第一种方法计算参数在最坏情况分布扰动下的敏感度，以明确哪些类型的偏移会对外部有效性构成威胁。第二种方法将分布偏移视为随机变量，能够评估平均鲁棒性（而非最坏情况鲁棒性）。该方法基于对单个数据集上多个估计量的稳定性分析，将抽样不确定性和分布不确定性整合到单个置信区间中。
英文摘要：
We discuss recently developed methods that quantify the stability and generalizability of statistical findings under distributional changes. In many practical problems, the data is not drawn i.i.d. from the target population. For example, unobserved sampling bias, batch effects, or unknown associations might inflate the variance compared to i.i.d. sampling. For reliable statistical inference, it is thus necessary to account for these types of variation. We discuss and review two methods that allow quantifying distribution stability based on a single dataset. The first method computes the sensitivity of a parameter under worst-case distributional perturbations to understand which types of shift pose a threat to external validity. The second method treats distributional shifts as random which allows assessing average robustness (instead of worst-case). Based on a stability analysis of multiple estimators on a single dataset, it integrates both sampling and distributional uncertainty into a single confidence interval.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
