Assessment of predictive accuracy is a critical aspect of evaluating and comparing models, algorithms or technologies that produce the predictions. In the field of medical diagnosis, receiver operating characteristic (ROC) curves have become the standard tool for this purpose and its use is becoming increasingly common in other fields such as finance, atmospheric science and machine learning. T...

Regression models are introduced into the receiver operating characteristic (ROC) analysis to accommodate effects of covariates, such as genes. If many covariates are available, the variable selection issue arises. The traditional induced methodology separately models outcomes of diseased and nondiseased groups; thus, separate application of variable selections to two models will bring barriers...

| This paper reports the diagnostic performance of observers in detecting abnormalities in computer-generated mammogram-like images. A mathematical model of the human breast is deened in which breast tissues are simulated by spheres of diierent sizes and densities. Images are generated by casting rays from a speciied source, through the model, and onto an image plane. Observer performance when ...

Department of Dietetics, Kaohsiung Municipal United Hospital, Kaohsiung, Taiwan, ROC Department of Seafood Science, National Kaohsiung Marine University, Kaohsiung, Taiwan, ROC Medical Laboratory, Kaohsiung Municipal United Hospital, Kaohsiung, Taiwan, ROC Doctoral Program in Physical Education, Taipei Physical Education College, Taipei, Taiwan, ROC Sport Science Research Center, National Taiwa...

Receiver operating characteristic (ROC) curves evaluate the discriminatory power of a continuous marker to predict a binary outcome. The most popular parametric model for an ROC curve is the binormal model, which assumes that the marker, after a monotone transformation, is normally distributed conditional on the outcome. Here, the authors present an alternative to the binormal model based on th...