ROC curves for regression

Regression Analysis for Correlated ROC Curves

Comparing ROC curves derived from regression models.

In constructing predictive models, investigators frequently assess the incremental value of a predictive marker by comparing the ROC curve generated from the predictive model including the new marker with the ROC curve from the model excluding the new marker. Many commentators have noticed empirically that a test of the two ROC areas often produces a non-significant result when a corresponding ...

ROC Curves for Steganalysts

There are different approaches in the literature for the assessment of steganographic algorithms and steganalytic attacks. In the early papers it was considered sufficient to show the existence of an effect for one or a few examples only. The more the area of steganography evolved, the more diverse became the goals and the harder to measure the improvements. Many branches of science are facing ...

Confidence Bands for ROC Curves

We address the problem of comparing the performance of classifiers. In this paper we study techniques for generating and evaluating confidence bands on ROC curves. Historically this has been done using one-dimensional confidence intervals by freezing one variable—the false-positive rate, or threshold on the classification scoring function. We adapt two prior methods and introduce a new radial s...

Resampling ROC curves

Receiver operating characteristic (ROC) curves are very popular for evaluating a diagnostic test or score performances in various decision making applications: medicine, marketing, credit scoring etc. The ROC curve provides a concise graphical representation of the trade off between sensitivity and specificity. We will focus here on supervised classification into two groups. Error rate estimati...