By Wojtek J. Krzanowski, David J. Hand
Since ROC curves became ubiquitous in lots of software parts, a few of the advances were scattered throughout disparate articles and texts. ROC Curves for non-stop facts is the 1st ebook completely dedicated to the topic, bringing jointly all of the correct fabric to supply a transparent realizing of ways to research ROC curves. the elemental conception of ROC curvesThe ebook first discusses the connection among the ROC curve and diverse functionality measures after which extends the speculation into perform by way of describing how ROC curves are envisioned. extra development at the idea, the authors current statistical exams for ROC curves and their precis information. they give thought to the impression of covariates on ROC curves, learn the real targeted challenge of evaluating ROC curves, and canopy Bayesian equipment for ROC research. targeted topicsThe textual content then strikes directly to extensions of the elemental research to deal with extra complicated occasions, reminiscent of the mix of a number of ROC curves and difficulties caused by way of the presence of greater than sessions. targeting layout and interpretation matters, it covers lacking facts, verification bias, pattern measurement selection, the layout of ROC stories, and the alternative of optimal threshold from the ROC curve. the ultimate bankruptcy explores functions that not just illustrate a number of the strategies but additionally reveal the very broad applicability of those options throughout varied disciplines. With approximately 5,000 articles released up to now when it comes to ROC research, the explosive curiosity in ROC curves and their research will proceed within the foreseeable destiny. Embracing this progress of curiosity, this well timed booklet will surely advisor current and destiny clients of ROC research.
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Extra info for ROC Curves for Continuous Data
2002) caution against using this approach unless the data have been checked carefully for consistency with the assumptions. In this regard, Zou and Hall (2000) propose both parametric and semiparametric transformation models that make the approach more defensible. The parametric method is the more straightforward. This requires a Box-Cox transformation of the classiﬁcation statistic to normality, with all parameters being estimated by maximum likelihood, followed by construction of the ROC curve from the transformed values.
4. SUMMARY INDICES OF THE ROC CURVE ✐ 29 In this case a suitable summary index of the ROC is the partial area under the curve, b y(x)dx. PAUC(a, b) = a However, while this seems a perfectly reasonable way of restricting attention to a portion of the fp range that is of particular interest or relevance, the problem is that both the maximum and minimum values depend on a and b which makes interpretation of any one value, and comparison of two or more values, somewhat problematic. On the other hand, the maximum and minimum values are easy to ﬁnd and this opens up the possibility of standardizing PAUC(a, b) values for direct comparison.
Likewise, for ROC analysis, it is useful to have such a standard model which can be adopted as a ﬁrst port of call in the expectation that it will provide a reasonable analysis in many practical situations and against which any specialized analysis can be judged. Such a benchmark is provided by the binormal model, in which we assume the scores S of the classiﬁer to have a normal distribution in each of the two populations P and N. , Krzanowski and Marriott, 1995, pp. 29-30). However, it is also approximately correct for a much wider set of measurement populations and classiﬁers.
ROC Curves for Continuous Data by Wojtek J. Krzanowski, David J. Hand