报告题目：Group-Average and Convex Clustering for Partially Heterogeneous Linear Regression

报告时间：2018年6月5日，星期二，上午9：00—10：30

报告地点：理科楼112

报告人：林路教授，山东大学

摘要:

In this paper, a subgroup least squares and a convex clustering are introduced for inferring a partially heterogenous linear regression that has potential application in the areas of precision marketing and precision medicine. The homogenous parameter and the subgroup-average of the heterogenous parameters can be consistently estimated by the subgroup least squares, without need of the sparsity assumption on the heterogenous parameters. The heterogenous parameters can be consistently clustered via the convex clustering. Unlike the existing methods for regression clustering, our clustering procedure is a standard mean clustering, although the model under study is a type of regression, and the corresponding algorithm only involves low dimensional parameters. Thus, it is simple and stable even if the sample size is large. The advantage of the method is further illustrated via simulation studies and the analysis of car sales data.

报告人简介：

林路教授是山东大学金融研究院副院长、博士生导师；南开大学获得博士学位后,先在南开大学任教，然后到山东大学任教至今；在高维统计、非参数和半参数统计以及金融统计等方面，取得许多重要的研究成果，在Annals of Statistics，Journal of Machine Learning Research等国际统计学和机器学习顶级刊物以及重要期刊发表研究论文90余篇；主持过多项国家自然科学基金课题、博士点专项基金课题、山东省自然科学基金重点项目等，获得过国家统计局颁发的统计科技进步二等奖，山东省优秀教学成果一等奖；是国家973项目、国家创新群体和教育部创新团队的核心成员，教育部应用统计专业硕士教育指导委员会成员，山东省政府参事。