Goodness of fit tests for the multiple logistic regression model

By David W. Hosmer Jr., Stanley Lemesbow. 1980

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Verified Uncertainty Calibration (2019, methodology): “Besides the calibration error, prior work also uses the Hosmer-Lemeshov test [37] and reliability diagrams [4, 38] to evaluate calibration.”
Measuring Calibration in Deep Learning (2019, background): “The Hosmer-Lemeshow test is a popular hypothesis test for assessing whether a model’s predictions significantly deviate from perfect calibration (Hosmer and Lemesbow, 1980).”
What Have We Learned about the Effects of the COVID-19 Pandemic on Consumer Behavior? (2021, background): “05, implying that the model’s estimates adjust to the data to an acceptable level [88].”
Grizzly bears and forestry II. Distribution of grizzly bear foods in clearcuts of west-central Alberta, Canada (2004, methodology): “We assessed fit and predictive accuracy of training data using Hosmer and Lemeshow (1980, 1989) goodness-of-fit w2-tests (Ĉ) and receiver operator characteristic (ROC) area under the curve estimates.”
Tests for goodness of fit in ordinal logistic regression models (2016, methodology): “An ordinal version of the Hosmer–Lemeshow test We consider a test based on the Hosmer–Lemeshow test [4] for binary logistic regression.”
Twopm: Two-Part Models (2015, methodology): “The two-part model has a commonly used counterpart for count data called the “hurdle” model (see Cameron and Trivedi [2013]; Jones [1989]; and Hilbe [2005]).”