Family-wise error rate control in Gaussian graphical model selection via Distributionally Robust Optimization
Methodology
2022-06-10 v1 Applications
Abstract
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.
Cite
@article{arxiv.2201.12441,
title = {Family-wise error rate control in Gaussian graphical model selection via Distributionally Robust Optimization},
author = {Chau Tran and Pedro Cisneros-Velarde and Sang-Yun Oh and Alexander Petersen},
journal= {arXiv preprint arXiv:2201.12441},
year = {2022}
}