Krzanowski W.J., Hand D.J. ROC Curves for Continuous Data

Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability (Book 111).
— Chapman and Hall/CRC, 2009. — 235 p. — ISBN: 1439800219, 978-1439800218.
Since ROC curves have become ubiquitous in many application areas, the various advances have been scattered across disparate articles and texts. ROC Curves for Continuous Data is the first book solely devoted to the subject, bringing together all the relevant material to provide a clear understanding of how to analyze ROC curves.
The fundamental theory of ROC curves.
The book first discusses the relationship between the ROC curve and numerous performance measures and then extends the theory into practice by describing how ROC curves are estimated. Further building on the theory, the authors present statistical tests for ROC curves and their summary statistics. They consider the impact of covariates on ROC curves, examine the important special problem of comparing two ROC curves, and cover Bayesian methods for ROC analysis.
Special topics.
The text then moves on to extensions of the basic analysis to cope with more complex situations, such as the combination of multiple ROC curves and problems induced by the presence of more than two classes. Focusing on design and interpretation issues, it covers missing data, verification bias, sample size determination, the design of ROC studies, and the choice of optimum threshold from the ROC curve. The final chapter explores applications that not only illustrate some of the techniques but also demonstrate the very wide applicability of these techniques across different disciplines.
With nearly 5,000 articles published to date relating to ROC analysis, the explosive interest in ROC curves and their analysis will continue in the foreseeable future. Embracing this growth of interest, this timely book will undoubtedly guide present and future users of ROC analysis.Background.
Classification.
Classifier performance assessment.
The ROC curve.
Population ROC curves.
The ROC curve.
Slope of the ROC curve and optimality results.
Summary indices of the ROC curve.
The binormal model.
Estimation.
Preliminaries: classification rule and error rates.
Estimation of ROC curves.
Sampling properties and confidence intervals.
Estimating summary indices.
Further inference on single curves.
Tests of separation of P and N population scores.
Sample size calculations.
Errors in measurements.
ROC curves and covariates.
Covariate adjustment of the ROC curve.
Covariate adjustment of summary statistics.
Incremental value.
Matching in case-control studies.
Comparing ROC curves.
Comparing summary statistics of two ROC curves.
Comparing AUCs for two ROC curves.
Comparing entire curves.
Identifying where ROC curves differ.
Bayesian methods.
General ROC analysis.
Meta-analysis.
Uncertain or unknown group labels.
Beyond the basics.
Alternatives to ROC curves.
Convex hull ROC curves.
ROC curves for more than two classes.
Other issues.
Design and interpretation issues.
Missing values.
Bias in ROC studies.
Choice of optimum threshold.
Medical imaging.
Substantive applications.
Machine learning.
Atmospheric sciences.
Geosciences.
Biosciences.
Finance.
Experimental psychology.
Sociology.
Appendix: ROC software.