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 ...

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 ...

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 ...

Nicholas J. Horton, Nan M. Laird, Maximum Likelihood Analysis of Logistic Regression Models with Incomplete Covariate Data and Auxiliary Information, Biometrics, Vol ... T. M. (1991b). Manual for the ...

Robert D. Gibbons, Donald Hedeker, Random Effects Probit and Logistic Regression Models for Three-Level Data, Biometrics, Vol. 53, No. 4 (Dec., 1997), pp. 1527-1537. Link ... The Television School and ...