ROC Curves for Continuous Data
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 fundamenta
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accuracy adjustment allocation anagrams applications approach appropriate assessment assigned assume assumption asymptotic AUC values axis Bayesian bias binary binormal model biomarkers chance diagonal Chapter classification rule classification score classification threshold classifier comparing computational confidence intervals cost covariates data set defined denote density derived described developed diagnostic disease distribution function empirical ROC curve error rate example false positive rate frequentist given gold standard test hence imputation inference linear logistic regression Lorenz curve machine learning Mann-Whitney maximum likelihood MCAR mean measurement errors methodology methods Metz missing values nonparametric normal distribution null hypothesis objects observations obtained Obuchowski optimal parameters PAUC performance placement values plot prediction probability problem proportion of class quadratic classifier random randomly ratio receiver operating characteristic regression ROC analysis Section specific studies tion training set transformation true positive rate variable variance vector verification sample