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.

翻译：最近,基于分布式强力优化框架的精确矩阵估算的特例被证明相当于图形拉索。 从这一提法中,提出了一种选择正规化术语的方法,即用于图形模型选择的方法。在这项工作中,我们建立了通过DRO配方选择图形模型的置信度与估算假边缘的无症状家庭错误率之间的理论联系。模拟实验和真实数据分析表明即使在有限的样本中也存在无症状家庭错误率控制行为的实用性。