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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 contributi
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