Abstract
ROC (Receiver Operating Characteristic) analyses are considered under a variety of assumptions concerning the distributions of a measurement X 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 w 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 c used for classification as well as the error characteristics used to assess the quality of the classification.
Funder
Natural Science and Engineering Research council of Canada
Subject
General Physics and Astronomy
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