The receiver operating characteristic (ROC) curve, the positive predictive value (PPV) curve and the negative predictive value (NPV) curve are three measures of performance for a continuous diagnostic biomarker. The ROC, PPV and NPV curves are often estimated empirically to avoid assumptions about the distributional form of the biomarkers. Recently, there has been a push to incorporate group se...

Multivariate ROC curve models that include an interaction term between biomarker type and false positive rate are important in comparative biomarker studies, because such interaction allows ROC curves of different biomarkers to cross each other. However, there has been limited work in drawing inference for comparing multivariate ROC curves, especially when interaction terms are present. In this...

RATIONALE AND OBJECTIVES Receiver operating characteristic (ROC) curves are ubiquitous in the analysis of imaging metrics as markers of both diagnosis and prognosis. While empirical estimation of ROC curves remains the most popular method, there are several reasons to consider smooth estimates based on a parametric model. MATERIALS AND METHODS A mixture model is considered for modeling the di...

The ROC curve is one of the most common statistical tools useful to assess classifier performance. The selection of the best classifier when ROC curves intersect is quite challenging. A novel approach for model comparisons when ROC curves show intersections is proposed. In particular, the relationship between ROC orderings and stochastic dominance is investigated in a theoretical framework and ...

Abstract Throughout science and technology, receiver operating characteristic (ROC) curves associated area under the curve ( $$\mathrm{AUC}$$ AUC ) measures constitute powerful tools for assessing predictive abilities of features, markers tests in binary classification problems. Despite its immense popularity...

ROC curves and cost curves are two popular ways of visualising classifier performance, finding appropriate thresholds according to the operating condition, and deriving useful aggregated measures such as the area under the ROC curve (AUC) or the area under the optimal cost curve. In this note we present some new findings and connections between ROC space and cost space, by using the expected lo...

This paper shows that ROC curves, as a method of visualizing classifier performance, are inadequate for the needs of Artificial Intelligence researchers in several significant respects, and demonstrates that a different way of visualizing performance – the cost curves introduced by Drummond and Holte at KDD’2000 – overcomes these deficiencies.

ROC plots are a common data representation for drawing conclusions from behavioral data about underlying mental representations and processes. According to broadly accepted conventions, the curvature, symmetry and detailed patterns of single curves are indicative of whether processing is mediated by continuous latent strengths, by discrete states, or by a dual-process mixture of the two. These ...