论文标题
使用排名的样品对ROC曲线下面积的经验可能性推断
Empirical Likelihood Inference for Area under the ROC Curve using Ranked Set Samples
论文作者
论文摘要
接收器操作特征曲线（AUC）下的区域是评估连续规模诊断测试在二元分类方面的性能的有用工具。在本文中，我们提出了一种经验可能性（EL）方法，以根据排名集采样（RSS）收集的数据为AUC构建置信区间。所提出的基于EL的方法可以推断现有非参数方法中需要的假设，并利用RSS的采样效率。我们表明，对于平衡和不平衡的RSS，基于EL的点估计是Mann-Whitney统计量，并且可以从缩放的卡方分布中获得置信区间。模拟研究和两项关于糖尿病和慢性肾脏疾病数据的案例研究表明，使用拟议的方法和RSS可以对AUC进行更有效的推断。
The area under a receiver operating characteristic curve (AUC) is a useful tool to assess the performance of continuous-scale diagnostic tests on binary classification. In this article, we propose an empirical likelihood (EL) method to construct confidence intervals for the AUC from data collected by ranked set sampling (RSS). The proposed EL-based method enables inferences without assumptions required in existing nonparametric methods and takes advantage of the sampling efficiency of RSS. We show that for both balanced and unbalanced RSS, the EL-based point estimate is the Mann-Whitney statistic, and confidence intervals can be obtained from a scaled chi-square distribution. Simulation studies and two case studies on diabetes and chronic kidney disease data suggest that using the proposed method and RSS enables more efficient inference on the AUC.
