compute the overlap in data sets which are commonly fitted by logistic regression models. The overlap is the number of … observations that would need to be removed to obtain complete or quasicomplete separation, i.e. the situation where the logistic … regression parameters are no longer identifiable and the maximum likelihood estimate does not exist. It turns out that the …

compute the overlap in data sets which are commonly fitted by logistic regression models. The overlap is the number of … observations that would need to be removed to obtain complete or quasicomplete separation, i.e. the situation where the logistic … regression parameters are no longer identifiable and the maximum likelihood estimate does not exist. It turns out that the …

The objective of this paper is to compare the performance of two predictive radiological models, logistic regression … significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic … regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap …

compute the overlap in data sets which are commonly fitted by logistic regression models. The overlap is the number of … observations that would need to be removed to obtain complete or quasicomplete separation, i.e. the situation where the logistic … regression parameters are no longer identifiable and the maximum likelihood estimate does not exist. It turns out that the …