Open Access
2021 Multicarving for high-dimensional post-selection inference
Christoph Schultheiss, Claude Renaux, Peter Bühlmann
Author Affiliations +
Electron. J. Statist. 15(1): 1695-1742 (2021). DOI: 10.1214/21-EJS1825


We consider post-selection inference for high-dimensional (generalized) 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. Furthermore, we extend existing concepts to group inference and illustrate the applicability of the methodology also for generalized linear models.

Funding Statement

The research of C. Schultheiss and P. Bühlmann was supported in part by the European Research Council under the Grant Agreement No 786461 (CausalStats – ERC-2017-ADG).


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Christoph Schultheiss. Claude Renaux. Peter Bühlmann. "Multicarving for high-dimensional post-selection inference." Electron. J. Statist. 15 (1) 1695 - 1742, 2021.


Received: 1 June 2020; Published: 2021
First available in Project Euclid: 26 March 2021

Digital Object Identifier: 10.1214/21-EJS1825

Keywords: Group inference , High-dimensional data , Lasso , linear model , logistic regression , sample splitting , Variable selection

Vol.15 • No. 1 • 2021
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