报告人 ：栗家量（新加坡国立大学）

时间：2023年5月15日 下午4:00开始

地址：数统学院LD402

摘要：Computation of hypervolume under ROC manifold (HUM) is necessary to evaluate biomarkers for their capability to discriminate among multiple disease types or diagnostic groups. However, the original definition of HUM involves multiple integration and thus a medical investigation for multi-class ROC analysis could suffer from huge computational cost when the formula is implemented naively. We introduce a novel graph-based approach to compute HUM efficiently in this paper. The computational method avoids the time-consuming multiple summation when sample size or the number of categories is large. We conduct extensive simulation studies to demonstrate the improvement of our method over existing R packages. We apply our method to two real biomedical data sets to illustrate its application.

（注：在讲座结束后，栗教授将介绍新加坡国立大学统计与数据科学系的一些招生项目的政策，希望感兴趣的本科生和研究生积极参与。）

简介：栗家量，教授，于中国科学技术大学统计系取得本科学士，后于美国威斯康星大学取得统计学博士学位。现任职于新加坡国立大学统计与数据科学系教授(Full Professor)。他最近的研究方向包括变点检测、personalized medicine、diagnostic medicine、survival analysis、structural equation model、nonparametric model，发表科研论文200多篇。他是美国统计学会(ASA)和国际数理统计学会(IMS)的当选会士(Fellow)、Elected member of ISI，担任Annals of Applied Statistics, Biometrics和Lifetime Data Analysis期刊的Associate Editor。

邀请人：夏小超

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