Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

This book also explains the differences and similarities between the many generalizations of the logistic regression model. The following topics are covered: binary logit analysis, logit analysis of ...

If the signal to noise ratio is low (it is a ‘hard’ problem) logistic regression is likely to perform best. In technical terms, if the AUC of the best model is below 0.8, logistic very clearly ...

Selection of a subset of meaningful covariates for a statistical model is an important and often time-consuming task in model building. Lawless and Singhal (1978, Biometrics 34, 318-327) proposed a ...

Melissa Dowd Begg, Stephen Lagakos, On the Consequences of Model Misspecification in Logistic Regression, Environmental Health Perspectives, Vol. 87 (Jul., 1990), pp. 69-75 ...