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Hypergraph Variable Selection with False Discovery Rate Control
[Submitted on 18 Jun 2026] · 2026-06-19 · via stat updates on arXiv.org

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Abstract:Variable selection methods that control the false discovery rate often lose power when predictors exhibit complex dependence structures. We previously showed that selecting hierarchically clustered groups of predictors can mitigate this issue while maintaining false discovery rate control. When correlations are less structured, however, overlapping predictor sets may be more effective. We introduce a generalized false discovery rate for hypotheses defined on sets of predictors and propose a hypergraph-based selection method. This approach achieves higher power across diverse settings while preserving rigorous false discovery rate control.

Submission history

From: Toby Kenney [view email]
[v1] Thu, 18 Jun 2026 17:34:32 UTC (578 KB)