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arXiv · 2201.12441

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

Chau Tran·Pedro Cisneros-Velarde·Sang-Yun Oh·Alexander Petersen
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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.

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Chau Tran, Pedro Cisneros-Velarde, Sang-Yun Oh, Alexander Petersen. 2022-01-28. Family-wise error rate control in Gaussian graphical model selection via Distributionally Robust Optimization. https://doi.org/10.1002/sta4.477

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