论文标题
用于高维后推断的多农用
Multicarving for high-dimensional post-selection inference
论文作者
论文摘要
我们考虑对高维（广义）线性模型的选择后推断。数据雕刻（Fithian等，2014）是执行此任务的有前途的技术。但是，它遭受了模型选择器的不稳定性的影响，因此可能导致可复制性差，尤其是在高维环境中。我们提出的多巢方法启发了多层次，以提高稳定性和可复制性。此外，我们将现有概念扩展到小组推理，并说明了该方法对通用线性模型的适用性。
We consider post-selection inference for high-dimensional (generalized) linear models. Data carving (Fithian et al., 2014) is a promising technique to perform this task. However, it suffers from the instability of the model selector and hence, may lead to poor replicability, especially in high-dimensional settings. We propose the multicarve method inspired by multisplitting to improve upon stability and replicability. Furthermore, we extend existing concepts to group inference and illustrate the applicability of the methodology also for generalized linear models.
