Family‐wise error rate control in Gaussian graphical model selection via distributionally robust optimization

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....

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Bibliographic Details
Published in:Stat (International Statistical Institute) 2022-12, Vol.11 (1), p.n/a
Main Authors: Tran, Chau, Cisneros‐Velarde, Pedro, Oh, Sang‐Yun, Petersen, Alexander
Format: Article
Language:English
Subjects:
Asymptotic properties
Confidence intervals
distributionally robust optimization
Estimation
family‐wise error rate
Gaussian graphical model
graphical lasso
Optimization
Regularization
Robustness
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Summary: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.
ISSN:2049-1573
2049-1573
DOI:10.1002/sta4.477