Multicarving for high-dimensional post-selection inference

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-dimen...

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Bibliographic Details
Published in:arXiv.org 2021-02
Main Authors: Schultheiss, Christoph, Renaux, Claude, Bühlmann, Peter
Format: Article
Language:English
Subjects:
Generalized linear models
Inference
Statistical models
Online Access:Get full text
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Summary: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.
ISSN:2331-8422