ROC Analyses Based on Measuring Evidence

2021/03/01 by Luai Al Labadi, Michael Evans, Labadi, Luai Al +3
Computer Science · #Anomaly Detection Techniques and Applications #Applications (stat.AP) #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Rough Sets and Fuzzy Logic

paper · pdf · doi:10.48550/arxiv.2103.00772

openalex publication_date 2021/03/01 · openalex created_date 2021/03/15 · openalex updated_date 2026/07/28

Abstract

ROC 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 as well as the cutoff c used for classification and the associated error characteristics.

Citations

• Checking the Model and the Prior for the Constrained Multinomial

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