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arXiv Statistics ML By Ebrahim Khaled Ebrahim, Osama Abd El-Aziz Hussein, Ahmed El-Kotory

[EDGE] A grouped calibration test for logistic regression that tolerates a few corrupted records

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The paper introduces EDGE, a grouped calibration test for logistic regression that remains robust when a few records are corrupted. EDGE projects grouped residuals onto a small basis of calibration shapes, keeping degrees of freedom independent of group count, which allows fine‑grained power or coarse protection against outliers. In simulations and external validation, EDGE matched or exceeded the power of existing tests while maintaining false‑alarm rates below 10% even with corrupted data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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