报告题目：Change surface regression for nonlinear subgroup identification

报告人：栗家量

时间：2025年9月29日(周一)上午10:30-11:30

地点：黑料网 创新港涵英楼8121报告厅

报告人简介：

栗家量，新加坡国立大学统计与数据科学系教授，于2006年在美国威斯康星大学麦迪逊分校获得统计学博士学位。研究兴趣包括金融统计、统计学习、 生存分析等。研究结果发表于Journal of Econometrics, Journal of the American Statistical Association, Journal of the Royal Statistical Society-Series B等顶级期刊,并著有专著1本。他是美国统计学会（ASA）Fellow，美国数理统计协会（IMS）Fellow, 国际统计协会（ISI）Elected Member。担任Annals of Applied Statistics, Lifetime Data Analysis等国际权威期刊的副主编。

摘要：

We formulate a novel change surface model as a model-based approach for multiple subgroup identification in complex datasets. A key feature of our approach is its ability to accommodate nonlinear subgroup divisions, providing a clearer understanding of dynamic associations. Furthermore, our model effectively handles high-dimensional data through a doubly penalized approach, ensuring both interpretability and adaptability. We propose an iterative 2-stage method that combines a change point detection technique in the first stage with a smoothed local adaptive majorize-minimization algorithm for surface regression in the second stage. Performance of the proposed methods is evaluated through extensive numerical studies. Application of our method to the IWPC dataset leads to significant new findings.