Order-restricted inference for clustered ROC data with application to fingerprint matching accuracy.

ABSTRACT: Receiver operating characteristic (ROC) curve is commonly used to evaluate and compare the accuracy of classification methods or markers. Estimating ROC curves has been an important problem in various fields including biometric recognition and diagnostic medicine. In real applications, classification markers are often developed under two or more ordered conditions, such that a natural stochastic ordering exists among the observations. Incorporating such a stochastic ordering into estimation can improve statistical efficiency (Davidov and Herman, 2012). In addition, clustered and correlated data arise when multiple measurements are gleaned from the same subject, making estimation of ROC curves complicated due to within-cluster correlations. In this article, we propose to model the ROC curve

SUBMITTER: Zhang W 

PROVIDER: S-EPMC9680977 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature