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arXiv Statistics ML By Ebrahim Khaled Ebrahim

DeepGOF-1: A Pretrained Convolutional Goodness-of-Fit Test for Logistic Regression with a Computable Consistency Certificate

Read the original on arXiv Statistics ML →

DeepGOF-1 introduces a pretrained convolutional network as a goodness‑of‑fit test for logistic regression, where the network reads a grid of standardized residuals as an image and outputs a test statistic. The test is fully calibrated via the analyst’s own bootstrap, ensuring the nominal level is maintained regardless of the network’s training. The authors prove exactness under pivotality, asymptotic exactness without it, and provide a computable consistency certificate from the frozen weights, demonstrating superior stability and power across multiple benchmarks and sample sizes.

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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