报告题目: A network approach to compute hypervolume under ROC

报 告 人：栗家量教授（新加坡国立大学）

报告时间：2023年6月1日（星期四）14：00-15：00

报告地点：25教14楼1403报告厅

参加人员：教师、研究生、本科生

报告摘要：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.

个人简介：栗家量，新加坡国立大学统计与应用概率系教授，同时在杜克大学-新加坡国大医学院与新加坡眼科研究所任兼职教授。栗教授于2001年毕业于中国科学技术大学(University of Science and Technology of China)获得理学学士学位；2006年在美国威斯康星大学-麦迪逊分校(Universityof Wisconsin, Madison)获得统计学博士学位。目前研究兴趣是半参数回归分析、纵向数据、高维数据、医疗诊断、生存分析；已发表包含《Annals of Statistics》、《Journal of the American Statistical Association》、《Journal of the Royal Statistical Society Series B》、《Journal of Economentrics》等顶级期刊在内的论文160余篇；曾担任《Biometrics》、《Lifetime Data Analysis》等国际权威期刊的副主编。详情请见其个人主页：https://blog.nus.edu.sg/jialiang/