# Family-wise error rate control in Gaussian graphical model selection via Distributionally Robust Optimization

arXiv: https://arxiv.org/abs/2201.12441 · HTML: https://mlsanity.com/paper/2201.12441/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2022.

## Built on by (most cited first, 1 of 1)

- [Relaxed Sparsest-Permutation Formulation for Causal Discovery at Scale](https://mlsanity.com/paper/2605.05568/) (2026)

## Abstract (arXiv)

Recently, a special case of precision matrix estimation based on a distributionally robust optimization (DRO) framework has been shown to be equivalent to the graphical lasso. From this formulation, a method for choosing the regularization term, i.e., for graphical model selection, was proposed. In this work, we establish a theoretical connection between the confidence level of graphical model selection via the DRO formulation and the asymptotic family-wise error rate of estimating false edges. Simulation experiments and real data analyses illustrate the utility of the asymptotic family-wise error rate control behavior even in finite samples.