The receiver operating characteristic curve is widely applied in measuring the per-formance of diagnostic tests. Many direct and indirect approaches have been proposed for modelling the ROC curve, and because of its tractability, the Gaussian distribution has typically been used to model both populations. We propose using a Gaussian mix-ture model, leading to a more flexible approach that better accounts for atypical data. Monte Carlo simulation is used to circumvent the issue of absence of a closed-form. We show that our method performs favourably when compared to the crude binor-mal curve and to the semi-parametric frequentist binormal ROC using the famous LABROC procedure
The area under the receiver operating characteristic (ROC) curve is often used to summarize and comp...
The receiver operating characteristic (ROC) curve is commonly used to evaluate the accuracy of a dia...
A well established technique to improve the classification performances is to combine more classifie...
Rationale and Objectives: ROC curves are ubiquitous in the analysis of imaging metrics as markers of...
The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic te...
Receiver operating characteristic (ROC) analysis, which yields indices of accuracy such as the area ...
A Receiver Operating Characteristic (ROC) curve reflects the performance of a system which decides b...
The Receiver Operating Characteristic curve (ROC) is frequently used in medical studies to assess th...
A receiver operating characteristic (ROC) curve is a plot of two survival functions, derived separat...
A two-parameter exponential equation for modeling a receiver operating characteristic (ROC) curve is...
In biomedical research, biomarkers (diagnostic tests) are used in distinguishing healthy and disease...
Receiver Operating Characteristic (ROC) Curve is a widely used classification technique in Medical D...
• This work overviews some developments on the estimation of the Receiver Operating Characteristic (...
A method for applying generalized ordinal regression models to categorical rating data to estimate a...
The receiver operating characteristics (ROC) analysis is commonly used in clinical settings to check...
The area under the receiver operating characteristic (ROC) curve is often used to summarize and comp...
The receiver operating characteristic (ROC) curve is commonly used to evaluate the accuracy of a dia...
A well established technique to improve the classification performances is to combine more classifie...
Rationale and Objectives: ROC curves are ubiquitous in the analysis of imaging metrics as markers of...
The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic te...
Receiver operating characteristic (ROC) analysis, which yields indices of accuracy such as the area ...
A Receiver Operating Characteristic (ROC) curve reflects the performance of a system which decides b...
The Receiver Operating Characteristic curve (ROC) is frequently used in medical studies to assess th...
A receiver operating characteristic (ROC) curve is a plot of two survival functions, derived separat...
A two-parameter exponential equation for modeling a receiver operating characteristic (ROC) curve is...
In biomedical research, biomarkers (diagnostic tests) are used in distinguishing healthy and disease...
Receiver Operating Characteristic (ROC) Curve is a widely used classification technique in Medical D...
• This work overviews some developments on the estimation of the Receiver Operating Characteristic (...
A method for applying generalized ordinal regression models to categorical rating data to estimate a...
The receiver operating characteristics (ROC) analysis is commonly used in clinical settings to check...
The area under the receiver operating characteristic (ROC) curve is often used to summarize and comp...
The receiver operating characteristic (ROC) curve is commonly used to evaluate the accuracy of a dia...
A well established technique to improve the classification performances is to combine more classifie...