Goodness-of-fit tests for logistic regression are routine in clinical risk modelling, yet the classical tests say whether a model misfits, not where. A pretrained network is not a test until its level, power and blind spots are established. We audit DeepGOF-1, which renders the residuals of a fitted logistic model as a...
Ebrahim Khaled Ebrahim, Osama Abd El-Aziz Hussein, Ahmed El-Kotory· 0 citations
EDGE, the test proposed here, projects the grouped residuals onto a small basis of calibration shapes that do not depend on the number of groups, so the partition becomes a setting, fine-grained for power and coarse when records may be wrong, and its reference needs no resampling.
Ebrahim Khaled Ebrahim, Osama Abd El-Aziz Hussein, Ahmed El-Kotory· 1 citation
Tests for heteroscedasticity in linear regression lose their level or power in three situations: when the data contain outliers, when the variance is not monotone, and when it changes along a variable outside the regressors, such as time. The proposed tests sort the observations by any chosen variable, split them into...
Ahmed El-Kotory, Ebrahim Khaled Ebrahim· 0 citations
Goodness-of-fit tests for logistic regression are least reliable where they are most needed: at small samples their levels drift from the nominal one, and combining them worsens the drift. We propose a test whose statistic is a convolutional network, trained once on simulated departures, that reads misfit as a picture:...
A probabilistic binary classifier is judged almost everywhere by discrimination - accuracy, the ROC curve, the area under it. Every such criterion is invariant to a monotone distortion of the predicted probabilities, so a classifier can rank perfectly and still return probabilities that are badly wrong. Calibration is...
Ebrahim Khaled Ebrahim, Ahmed El-Kotory· 1 citation
This thesis studies classical GOF tests for binary logistic regression under both grouped and sparse data, comparing about 30 statistical tests and machine-learning calibration algorithms and concludes that model assessment requires a combination of several powerful statistical tests alongside careful visual inspection...
The corrected law is derived and the shrinkage-corrected Hosmer-Lemeshow test is defined, which subtracts an estimate of that non-centrality induced by shrinkage, restoring the maximum likelihood reference exactly to first order, and is made valid by prepivoting at a power cost the authors measure.
Goodness-of-fit assessment for the binary logistic regression model is difficult when covariates are continuous: the data are effectively sparse, the classical Pearson and deviance tests fail, and practitioners rely on partition-based tests, such as the Hosmer-Lemeshow test, that group observations before comparing obs...
Ebrahim Khaled Ebrahim, Ahmed El-Kotory· 1 citation
This paper provides a unified taxonomy and a large-scale, reproducible simulation benchmark; more than twenty tests are implemented in the open-source R package ebrahim.gof, and translate these findings into practical, evidence-based guidance for assessing logistic regression fit.
Ebrahim Khaled Ebrahim, Ahmed El-Kotory· 0 citations
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