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This article presents a novel inference method for Gaussian graphical models dealing with erosely measured data, characterized by irregular sample sizes across different node pairs. The proposed method, named GI-JOE, addresses the limitations of existing approaches by providing edge-wise inference and FDR control that account for varying uncertainty levels due to erosely measured data. Through simulations and real-world neuroscience examples, the authors demonstrate the effectiveness of GI-JOE in improving

Author

Ping-Feng Xu

Language

EN