Localizing Global Discrepancies: Marginal Contributions and Contextual Anomaly Detection
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 equals the first Hoeffding/Hájek contribution; for smooth distributional functionals it is related a...
| Authors | Tommaso dorigo |
|---|---|
| Primary category | stat.ML |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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