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, that is, 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.

中文翻译：

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通过分布鲁棒优化在高斯图模型选择中进行族错误率控制

最近，基于分布式鲁棒优化 (DRO) 框架的精确矩阵估计的特殊情况已被证明等同于图形套索。从这个公式中，提出了一种选择正则化项的方法，即图模型选择方法。在这项工作中，我们建立了通过 DRO 公式选择图形模型的置信度与估计错误边缘的渐近族错误率之间的理论联系。模拟实验和真实数据分析说明了渐近族错误率控制行为的实用性，即使在有限样本中也是如此。