时 间：2025年6月6 日（周五）14：15 -15：00
报告人：陈雪蓉，西南财经大学青年杰出教授
地 点：普陀校区理科大楼A1514
主持人：史兴杰 华东师范大学副教授
摘 要：
In this paper, we propose a novel quantile regression modeling framework with a latent group structure, allowing samples drawn from a population consisting of groups with different conditional quantiles along with certain covariates. Different from most conventional modeling approaches for group identification, such as finite mixture models and threshold models, our new model is distribution free, allows the same individual to have different group affiliations on different covariates. Furthermore, it permits the group numbers and group structure of regression coefficients to be the same or different for different covariates. We identify the potential group structure for the quantile regression coefficients using the K-NN fused penalized method and recover the group boundaries using SVM method, after artificially assigning appropriate labels to different groups. The computational burden of our approach is significantly lower than the pairwise fused regularization method in Ma and Huang (2017). Moreover, unlike existing regularization methods, our method can analyze and explain the reasons for grouping and predict the group membership of new individuals based on the estimated group boundary. We establish the theoretical properties of the proposed estimators for group parameters and boundary parameters. Simulation studies and a real data analysis illustrate that the proposed methods perform well.
报告人简介：
陈雪蓉，西南财经大学青年杰出教授、博士生导师，国家级青年人才计划入选者，省级高层次人才入选者。中科院数学与系统科学研究院博士（联合培养），美国密苏里大学统计系、乔治城大学生物统计博士后，美国密歇根大学、香港城市大学、香港大学访问学者。论文发表于JASA, JBES,JCGS等统计学、计量经济学权威期刊。主持国家自然科学基金面上项目2项、青年项目、国家自然科学基金重点项目子课题、国家重点研发计划课题子课题各1项。曾荣获教育部“第八届高等学校科学研究优秀成果奖青年成果奖”。中国应用统计学会理事，资源与环境分会等分会常务理事。
