Time: 15:30-16:30pm, July. 5th, 2019, Friday
Venue: RoomA1514, Science Building, North Zhongshan Road Campus
Spreker: Associate Professor Jialiang Li, National University of Singapore
Abstract: Thresholding variable plays a crucial role in subgroup identification for personalized medicine. Most existing partitioning methods split the sample based on one predictor variable. In this paper, we consider setting the splitting rule from a combination of multivariate predictors, such as the latent factors, principle components, and weighted sum of predictors. Such a subgrouping method may lead to more meaningful partitioning of the population than using a single variable. In addition, our method is based on a change point regression model and thus yields straight forward model-based prediction results. After choosing a particular thresholding variable form, we apply a two-stage multiple change point detection method to determine the subgroups and estimate the regression parameters. We show that our approach can produce two or more subgroups from the multiple change points and identify the true grouping with high probability.In addition, our estimation results enjoy oracle properties. We design a simulation study to compare performances of our proposed and existing methods and apply them to analyze data sets from a Scleroderma trial and a breast cancer study.
Speaker’s Bio：Jialiang Li副教授，本科和硕士毕业于中国科学技术大学，博士毕业于美国威斯康辛大学麦迪逊分校。2006年至今在新加坡国立大学统计与应用概率系历任助理教授和副教授。在包括统计学顶级杂志Annals of Statistics, Journal of American Statistical Association和 Journal of the Royal Statistical Society, Series B等杂志上发表论文一百多篇。担任生物统计学著名杂志Biometrics的副主编。
