For the ROC curve and surface expressed as linear combination score random variables in realistic classification models, there are many research literature estimating linear coefficients to maximize the AUC (area under the ROC curve), VUS (volume unde...
For the ROC curve and surface expressed as linear combination score random variables in realistic classification models, there are many research literature estimating linear coefficients to maximize the AUC (area under the ROC curve), VUS (volume under the ROC surface) and pAUC (partial AUC) for a certain interval. In this paper, a standardized pAUC statistic is proposed to compare of other pAUCs which have the same length of intervals, so that an alternative pAUC approach method can be developed to estimate the linear coefficients corresponding to the interval with high discriminant power. The partial VUS approach method is extended to ROC surfaces for estimating the linear coefficient. Moreover, it is found that the optimal thresholds are included in these intervals obtained by these methods.