# Automated calibration for stability selection in penalised regression and graphical models

arXiv: https://arxiv.org/abs/2106.02521 · HTML: https://mlsanity.com/paper/2106.02521/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2021.

## Built on by (most cited first, 3 of 3)

- [On the Selection Stability of Stability Selection and Its Applications](https://mlsanity.com/paper/2411.09097/) (2024)
- [Stability Selection via Variable Decorrelation](https://mlsanity.com/paper/2505.20864/) (2025)
- [Robust variable selection for spatial point processes observed with noise](https://mlsanity.com/paper/2510.25550/) (2025)

## Abstract (arXiv)

Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an in-house stability score and accommodating a priori-known block structure (e.g. multi-OMIC) data. It applies to (LASSO) penalised regression and graphical models. Simulations show our approach outperforms non-stability-based and stability selection approaches using the original calibration. Application of multi-block graphical LASSO on real (epigenetic and transcriptomic) data from the Norwegian Women and Cancer study reveals a central/credible and novel cross-OMIC role of LRRN3 in the biological response to smoking. Proposed approaches were implemented in the R package sharp.