浙江大学数学系张朋研究员“基于分位数的方法推理的ROC和平均精度”大意为：难得一见目标检测问题的目标是尽早确定目标罕见。回想一下，精密和精密平均(AP)的措施，用于评估不同的检测方法三俗的表现。美联社还发现，在医学筛查和诊断测试，类似于为受试者工作特征(ROC)曲线，但更注重早期的情况下获得良好的性能指标。决策变量的基本分布一种流行的假设是二项分布。然而，现有的最大似然(ML)的方法估算正常参数所需要的实际观测。我们提出位数的一种方法和根据依赖于仅排名正态分布的位数另一个ML方式。模拟研究表明，这些方法的良好性能。以下是原文：

　　Inference on ROC and Average Precision Based on Methods of Quantile

　　The objective of a rare target detection problem is to identify the rare targets as early as possible. Recall, precision and average precision (AP) are three popular performance measures for evaluating different detection methods. The AP is found also a good performance measure in medical screening test and diagnostic test, similar as the receiver operating characteristic (ROC) curve but focusing more on earlier cases. A popular assumption for the underlying distributions of the decision variables is the binormal distribution. However, the existing maximum likelihood (ML) methods for estimating the normal parameters require the actual observations. We propose a method of quantile and another ML method based on quantiles of normal distributions that rely on ranking only. Simulation studies show the good performance of these approaches.（在职课程培训班网）