Multicarving for high-dimensional post-selection inference
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2020.
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Citing papers per year: 2022: 3; 2023: 2.
5 papers.
- 2022Selective inference for k-means clusteringcited by 21
- 2022Exact Selective Inference with Randomizationcited by 6
- 2023Selective inference using randomized group lasso estimators for general modelscited by 3
- 2022Black-box Selective Inference via Bootstrappingcited by 1
- 2023A parametric distribution for exact post-selection inference with data carvingcited by 0
- 2023Selective inference using randomized group lasso estimators for general models24 Jun 2023
- 2023A parametric distribution for exact post-selection inference with data carving21 May 2023
- 2022Exact Selective Inference with Randomization25 Dec 2022
- 2022Selective inference for k-means clustering29 Mar 2022
- 2022Black-box Selective Inference via Bootstrapping28 Mar 2022