论文标题
校准的推断：统计推断，既说明不确定性和分布不确定性
Calibrated inference: statistical inference that accounts for both sampling uncertainty and distributional uncertainty
论文作者
论文摘要
我们如何得出值得信赖的科学结论？一个标准是一项研究可以由独立团队复制。尽管复制至关重要，但可以说是不够的。如果由于某种原因对研究有偏见，而其他研究概括了该方法，那么发现可能始终是不正确的。有人认为，值得信赖的科学结论需要不同的证据来源。但是，不同的方法可能会共享偏见，因此很难判断结果的可信度。我们通过引入“分布不确定性模型”来形式化这个问题，其中，稠密的分布变化作为许多小型随机变化的叠加。分布扰动模型是在分布偏移的对称假设下产生的，并且严格弱于假设数据为I.I.D.从目标分布。我们表明，对单个数据集的稳定分析使我们能够构建置信区间，以解释采样不确定性和分布不确定性。
How can we draw trustworthy scientific conclusions? One criterion is that a study can be replicated by independent teams. While replication is critically important, it is arguably insufficient. If a study is biased for some reason and other studies recapitulate the approach then findings might be consistently incorrect. It has been argued that trustworthy scientific conclusions require disparate sources of evidence. However, different methods might have shared biases, making it difficult to judge the trustworthiness of a result. We formalize this issue by introducing a "distributional uncertainty model", wherein dense distributional shifts emerge as the superposition of numerous small random changes. The distributional perturbation model arises under a symmetry assumption on distributional shifts and is strictly weaker than assuming that the data is i.i.d. from the target distribution. We show that a stability analysis on a single data set allows us to construct confidence intervals that account for both sampling uncertainty and distributional uncertainty.
