Comparing ROC curves derived from regression models

Comparing ROC curves derived from regression models.

In constructing predictive models, investigators frequently assess the incremental value of a predictive marker by comparing the ROC curve generated from the predictive model including the new marker with the ROC curve from the model excluding the new marker. Many commentators have noticed empirically that a test of the two ROC areas often produces a non-significant result when a corresponding ...

Regression Analysis for Correlated ROC Curves

ROC Curves for Multivariate Biometric Matching Models

The biometric matching problem is a two class (“within” or “between”) classification problem where two types of errors (FRR and FAR) occur. While the receiver operating characteristic or ROC curve, which is a plot of FRR and FAR, can be easily obtained in the simple matching model, it is non-trivial to obtain in the multivariate matching model. Here the problem of obtaining ROC curves for sever...

Testing global memory models using ROC curves.

Global memory models are evaluated by using data from recognition memory experiments. For recognition, each of the models gives a value of familiarity as the output from matching a test item against memory. The experiments provide ROC (receiver operating characteristic) curves that give information about the standard deviations of familiarity values for old and new test items in the models. The...

A modified sign test for comparing paired ROC curves.

We develop a permutation test for assessing a difference in the areas under the curve (AUCs) in a paired setting where both modalities are given to each diseased and nondiseased subject. We propose that permutations be made between subjects specifically by shuffling the diseased/nondiseased labels of the subjects within each modality. As these permutations are made within modality, the permutat...