Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Benussi, Davide, Alongi, Ester, Banzato, Erika
Institution: University of Padua
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funder: Università degli Studi di Padova
OpenAlex ID: W7125515575
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