ROC Curves in Non-Parametric Location-Scale Regression Models

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 ...

Non parametric ROC summary statistics

Receiver operating characteristic (ROC) curves are useful statistical tools for medical diagnostic testing. It has been proved its capability to assess diagnostic marker’s ability to distinguish between healthy and diseased subjects and to compare different diagnostic markers. In this paper we introduce non parametric ROC summary statistics to assess a ROC curve across the entire range of FPFs ...

Regression Analysis for Correlated ROC Curves

Predictive Interval Models for Non-parametric Regression

Having a regression model, we are interested in finding two-sided intervals that are guaranteed to contain at least a desired proportion of the conditional distribution of the response variable given a specific combination of predictors. We name such intervals predictive intervals. This work presents a new method to find two-sided predictive intervals for non-parametric least squares regression...

Semi-parametric maximum likelihood estimates for ROC curves of continuous-scale tests.

In this paper, we propose a new semi-parametric maximum likelihood (ML) estimate of a receiver operating characteristic (ROC) curve that satisfies the property of invariance of the ROC curve and is easy to compute. We show that our new estimator is sqrt[n]-consistent and has an asymptotically normal distribution. Our extensive simulation studies show that the proposed method is efficient and ro...