Confidence Interval Estimation of an ROC Curve: An Application of Generalized Half Normal and Weibull Distributions
Confidence Interval Estimation of an ROC Curve: An Application of Generalized Half Normal and Weibull Distributions
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DOI：
10.1155/2015/934362
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发表时间：
2015-01-01
影响因子：
1.1
通讯作者：
Vardhan, R. Vishnu
中科院分区：
文献类型：
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作者：
Balaswamy, S.;Vardhan, R. Vishnu
最近，ROC 分析领域的工作在解释测试的准确性和确定最佳阈值方面引起了人们的关注。这种类型的 ROC 模型被称为双分布 ROC 模型，例如双正态、双指数、双逻辑等。然而，在实际情况中，我们会遇到本质上倾斜且尾部延长的数据。那么为了解决这个问题，需要通过涉及尺度和形状参数来解释测试的准确性。因此，本文重点提出一个 ROC 模型，该模型考虑了两个广义分布，有助于解释测试的准确性。此外，为建议的曲线构建置信区间；即曲线坐标（FPR、TPR）和准确度测量曲线下面积 (AUC)，这有助于解释曲线的变异性并提供特定特异性值的灵敏度，反之亦然。所提出的方法得到真实数据集和模拟研究的支持。
In the recent past, the work in the area of ROC analysis gained attention in explaining the accuracy of a test and identification of the optimal threshold. Such types of ROC models are referred to as bidistributional ROC models, for example Binormal, Bi-Exponential, Bi-Logistic and so forth. However, in practical situations, we come across data which are skewed in nature with extended tails. Then to address this issue, the accuracy of a test is to be explained by involving the scale and shape parameters. Hence, the present paper focuses on proposing an ROC model which takes into account two generalized distributions which helps in explaining the accuracy of a test. Further, confidence intervals are constructed for the proposed curve; that is, coordinates of the curve (FPR, TPR) and accuracy measure, Area Under the Curve (AUC), which helps in explaining the variability of the curve and provides the sensitivity at a particular value of specificity and vice versa. The proposed methodology is supported by a real data set and simulation studies.