论文标题
通过算法稳定性推理进行选择后推断
Post-Selection Inference via Algorithmic Stability
论文作者
论文摘要
当以数据驱动方式选择统计推断的目标时，经典理论提供的保证消失了。我们通过建立算法稳定性的框架，尤其是其起源于差异隐私领域的分支来解决选择后选择问题的解决方案。通过随机化选择，它可以作为一种定量度量来实现稳定性，足以获得经典置信区间的非平凡的选择后校正。重要的是，算法稳定性的基础直接转化为计算效率 - 我们的方法计算简单的校正选择性推断，而无需求助于Markov Chain Monte Carlo采样。
When the target of statistical inference is chosen in a data-driven manner, the guarantees provided by classical theories vanish. We propose a solution to the problem of inference after selection by building on the framework of algorithmic stability, in particular its branch with origins in the field of differential privacy. Stability is achieved via randomization of selection and it serves as a quantitative measure that is sufficient to obtain non-trivial post-selection corrections for classical confidence intervals. Importantly, the underpinnings of algorithmic stability translate directly into computational efficiency -- our method computes simple corrections for selective inference without recourse to Markov chain Monte Carlo sampling.
