Distribution-free ROC analysis using binary regression techniques

Distribution-free ROC analysis using binary regression techniques.

Receiver operating characteristic (ROC) regression methodology is used to identify factors that affect the accuracy of medical diagnostic tests. In this paper, we consider a ROC model for which the ROC curve is a parametric function of covariates but distributions of the diagnostic test results are not specified. Covariates can be either common to all subjects or specific to those with disease....

Distribution - Free Tests in Binary Regression Models

SUMMARY This paper proposes the use of Wilcoxon rank sum, Wilcoxon signed rank, and sign statistics for testing certain hypotheses representing zero effects in the linear logistic regression model. A simulation study provides insight into the alternatives for which each test is most powerful. An example with rea 1 data is gi ven for each tes t.

Distribution-Free Distribution Regression

‘Distribution regression’ refers to the situation where a response Y depends on a covariate P where P is a probability distribution. The model is Y = f(P ) + μ where f is an unknown regression function and μ is a random error. Typically, we do not observe P directly, but rather, we observe a sample from P . In this paper we develop theory and methods for distribution-free versions of distributi...

Regression Analysis for Correlated ROC Curves

ROC analysis in ordinal regression learning

Nowadays the area under the receiver operating characteristics (ROC) curve, which corresponds to the Wilcoxon–Mann–Whitney test statistic, is increasingly used as a performance measure for binary classification systems. In this article we present a natural generalization of this concept for more than two ordered categories, a setting known as ordinal regression. Our extension of the Wilcoxon–Ma...