ROC Analyses Based on Measuring Evidence Using the Relative Belief Ratio.

ROC (Receiver Operating Characteristic) analyses are considered under a variety of assumptions concerning the distributions of a measurement in two populations. These include the binormal model as well as nonparametric models where little is assumed about the form of distributions. The methodology is based on a characterization of statistical evidence which is dependent on the specification of prior distributions for the unknown population distributions as well as for the relevant prevalence of the disease in a given population. In all cases, elicitation algorithms are provided to guide the selection of the priors. Inferences are derived for the AUC (Area Under the Curve), the cutoff used for classification as well as the error characteristics used to assess the quality of the classification.