Multicarving for high-dimensional post-selection inference

Abstract

We consider post-selection inference for high-dimensional (gen-eralized) linear models. Data carving from Fithian, Sun and Taylor [10] 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. Fur-thermore, we extend existing concepts to group inference and illustrate the applicability of the methodology also for generalized linear models.