"Proper" binormal ROC curves: Theory and maximum-likelihood estimation
"Proper" binormal ROC curves: Theory and maximum-likelihood estimation
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DOI：
10.1006/jmps.1998.1218
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发表时间：
1999-03-01
影响因子：
1.8
通讯作者：
Pan, XC
中科院分区：
文献类型：
--
作者：
Metz, CE;Pan, XC
传统的双正态模型假设 ROC 数据背后有一对潜在的正态决策变量分布，多年来已成功用于拟合平滑的 ROC 曲线。然而，如果传统的双正态模型用于小数据集或类别边界分配不当的序数类别数据，则拟合的 ROC 中的“钩子”可能会在单位正方形的右上角或左下角附近出现。为了克服这种曲线拟合伪影，我们开发了一种“适当的”双正态模型和一种新算法，用于相应 ROC 曲线的最大似然 (ML) 估计。大量的模拟研究表明该算法高度可靠。当传统副正态 ROC 没有表现出“钩子”时，真副正态 ROC 曲线和传统副正态 ROC 曲线的 ML 估计实际上是相同的，但真副正态曲线对于所有数据集都具有单调斜率，包括传统模型产生简并拟合的数据集。 (C) 1999 年学术出版社。
The conventional binormal model, which assumes that a pair of latent normal decision-variable distributions underlies ROC data, has been used successfully for many years to fit smooth ROC curves. However, if the conventional binormal model is used for small data sets or ordinal-category data with poorly allocated category boundaries, a "hook" in the fitted ROC may be evident near the upper-right or lower-left corner of the unit square. To overcome this curve-fitting artifact, we developed a "proper" binormal model and a new algorithm for maximum-likelihood (ML) estimation of the corresponding ROC curves. Extensive simulation studies have shown the algorithm to be highly reliable. ML estimates of the proper and conventional binormal ROC curves are virtually identical when the conventional binormal ROC shows no "hook," but the proper binormal curves have monotonic slope for all data sets, including those for which the conventional model produces degenerate fits. (C) 1999 Academic Press.