浙江大學數(shù)學系張朋研究員“基于分位數(shù)的方法推理的ROC和平均精度”大意為：難得一見目標檢測問題的目標是盡早確定目標罕見�；叵胍幌拢�精密和精密平均(AP)的措施，用于評估不同的檢測方法三俗的表現(xiàn)。美聯(lián)社還發(fā)現(xiàn)，在醫(yī)學篩查和診斷測試，類似于為受試者工作特征(ROC)曲線，但更注重早期的情況下獲得良好的性能指標。決策變量的基本分布一種流行的假設(shè)是二項分布。然而，現(xiàn)有的最大似然(ML)的方法估算正常參數(shù)所需要的實際觀測。我們提出位數(shù)的一種方法和根據(jù)依賴于僅排名正態(tài)分布的位數(shù)另一個ML方式。模擬研究表明，這些方法的良好性能。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.（在職課程培訓班網(wǎng)）