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arXiv · 2601.15896

Leave-one-out testing for node-level differences in Gaussian graphical models

Davide Benussi·Ester Alongi·Erika Banzato
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Abstract

We study two-sample equality testing in Gaussian graphical models. Classical likelihood ratio tests on decomposable graphs admit clique-wise factorizations, offering limited localization and unstable finite-sample behaviour. We propose node-level inference via a leave-one-out Bartlett-adjusted test on a fully connected graph. The resulting increments have standard chi-square null limits, enabling calibrated significance for single nodes and fixed-size subsets. Simulations confirm validity, and a case study shows practical utility.

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Davide Benussi, Ester Alongi, Erika Banzato. 2026-01-22. Leave-one-out testing for node-level differences in Gaussian graphical models. https://arxiv.org/abs/2601.15896

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