  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.

Methodology