报告题目： A network approach to compute hypervolume under ROC manifold for multi-class biomarkers

报告人:栗家量 教授（新加坡国立大学）

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.

报告人简介：栗家量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。最近研究方向包括变点，personalized medicine, diagnostic medicine, survival analysis, structural equation model, nonparametric model.发表科研论文200多篇。他是ASA(美国统计学会)和IMS（数理统计学会）的选举会士Fellow.曾担任biometrics和lifetime data analysis的AE.

报告时间：2023年4月13日（周四）上午11：00-12：00

报告地址: 教二楼627

联系人: 胡涛