论文标题
高斯图形模型选择通过分布强大的优化选择的家庭错误率控制
Family-wise error rate control in Gaussian graphical model selection via Distributionally Robust Optimization
论文作者
论文摘要
最近，基于分布鲁棒优化（DRO）框架的精确矩阵估计的特殊情况已显示与图形套索相等。从该公式中，提出了一种选择正则化项的方法，即用于图形模型选择的方法。在这项工作中，我们通过DRO公式的图形模型选择的置信度水平与估计虚假边缘的渐近家庭错误率建立了理论上的联系。模拟实验和实际数据分析说明了即使在有限样本中，渐近家庭误差率控制行为的实用性也是如此。
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.
