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Localizing Global Discrepancies: Marginal Contributions and Contextual Anomaly Detection

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

Global goodness-of-fit and discrepancy statistics can establish that a sample departs from a reference distribution without identifying which observations drive the departure. We develop a framework for this localization problem by assigning to each observation its conditional or marginal contribution across random statistical contexts. This connects resampling diagnostics and data valuation to projection theory and event-level anomaly detection. For symmetric statistics, fixed-size replacement is exactly equivalent to centered conditional localization. For U-statistics, the addition score equ

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