Kernel estimators of the ROC curve are better than empirical
Kernel estimators of the ROC curve are better than empirical
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DOI：
10.1016/s0167-7152(99)00012-7
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发表时间：
1999-09-15
影响因子：
0.8
通讯作者：
Yong, Z
中科院分区：
文献类型：
--
作者：
Lloyd, CJ;Yong, Z
受试者工作特征（ROC）是一条用于总结二元决策规则性能的曲线。它可以用作为规则基础的诊断测量的基本分布函数来表示。Lloyd（1998）提出了从这些分布函数的核平滑估计ROC曲线，并给出了所得曲线估计量的偏差和标准差的渐近公式。本文比较了基于核的估计与完全经验估计的渐近精度。结果表明，经验估计是不足的核估计相比，这种不足是无界的样本容量的增加。使用单峰和双峰分布的模拟研究表明，在准确性的增益是显着的现实样本量。现在可以推荐基于核的ROC估计器。(C)1999 Elsevier Science B. V.保留所有权利。MSG.初级62 G 05; 60 F17;次级62 E20; 62 G20。
The receiver operating characteristic (ROC) is a curve used to summarise the performance of a binary decision rule. It can be expressed in terms of the underlying distributions functions of the diagnostic measurement that underlies the rule. Lloyd (1998) has proposed estimating the ROC curve from kernel smoothing of these distribution functions and has presented asymptotic formulas for the bias and standard deviation of the resulting curve estimator. This paper compares the asymptotic accuracy of the kernel-based estimator with the fully empirical estimator. It is shown that the empirical estimator is deficient compared to the kernel estimator and that this deficiency is unbounded as sample size increases. A simulation study using both unimodal and bimodal distributions indicates that the gains in accuracy are significant for realistic sample sizes. Kernel-based ROC estimators can now be recommended. (C) 1999 Elsevier Science B.V. All rights reserved. MSG. primary 62G05; 60F17; secondary 62E20; 62G20.