1 citations · 1 across the 5 of their papers we have counts for
9 papers · 1 filter
One Goodness-of-Fit Test Is Not Enough: Error-Controlled Localization of Misfit in Logistic Risk Models
Ebrahim Khaled Ebrahim, Ahmed El-Kotory, Osama Abd El-Aziz Hussein
A goodness-of-fit test tells whether a logistic risk model fits, but the analyst also needs to know what is wrong: an overall risk that is too high is repaired by updating the inte…
Foundation or Formula? A Simulation-Based Comparison of Google's TimesFM-3 and Classical Time-Series Models
Ebrahim Khaled Ebrahim, Somaia Mohamed Ali, Ahmed El-Kotory
Time-series foundation models such as Google's TimesFM-3 forecast series they were never trained on, but whether they should replace classical methods such as ARIMA and exponential…
Three-group variance-ratio tests for heteroscedasticity in linear regression: an exact null distribution and an outlier-resistant version
Ahmed El-Kotory, Ebrahim Khaled Ebrahim
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 chan…
Shrinkage invalidates the Hosmer-Lemeshow test: goodness of fit for penalized logistic regression, with an application to glaucoma diagnosis
Ebrahim Khaled Ebrahim
Clinical prediction models are increasingly fitted by penalized logistic regression, because collinearity or many candidate predictors makes maximum likelihood unstable or impossib…
[DeepGOF] Where Does a Logistic Risk Model Fail? An Audited Neural Goodness-of-Fit Test for Model Development and External Validation
Ebrahim Khaled Ebrahim, Osama Abd El-Aziz Hussein, Ahmed El-Kotory
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…
[EDGE] A grouped calibration test for logistic regression that tolerates a few corrupted records
Ebrahim Khaled Ebrahim, Ahmed El-Kotory, Osama Abd El-Aziz Hussein
The Hosmer-Lemeshow calibration test groups patients by predicted risk for a valid reference and loses power that more groups cannot recover. Later tests weight each record's resid…
Goodness-of-Fit Tests and Calibration Machine-Learning Algorithms for Logistic Regression with Sparse Data
Ebrahim Khaled Ebrahim
Assessing the goodness-of-fit of a logistic regression model is a critical prerequisite before the model is used for inference. However, goodness-of-fit (GOF) tests such as the chi…
Benchmarking Goodness-of-Fit and Calibration Algorithms for Logistic Regression Classifiers: A Large-Scale Simulation Study under Sparse Data
Ebrahim Khaled Ebrahim, Ahmed El-Kotory
Binary logistic regression is among the most widely used classification algorithms, yet a classifier is only trustworthy if its predicted probabilities are well calibrated. The cla…
A modified Hosmer-Lemeshow goodness-of-fit test for asymmetric links: second-order power and robustness
Ebrahim Khaled Ebrahim, Ahmed El-Kotory, Ibrahim Galal Khattab
The Hosmer-Lemeshow test is the routine check of fit for logistic regression, yet it is weak against a wrong link, and the directed tests that are stronger lose their level when a…