学术沙龙主题:Robust Subgroup Analysis for Network-Linked Data

报告人:东北师范大学教授、博士生导师

报告时间:2022年11月15日(周二)；上午10:00—11:30

报告地点：腾讯会议ID：723164172

报告人简介：朱文圣，东北师范大学伟德国际BETVlCTOR教授、博士生导师、副院长。2006年博士毕业于东北师范大学，2008-2010年在耶鲁大学做博士后研究，2015-2017年访问北卡罗来纳大学教堂山分校。中国现场统计研究会计算统计分会副理事长、旅游大数据分会副理事长、数据科学与人工智能分会秘书长，吉林省现场统计研究会秘书长。研究兴趣有生物统计学、精准医疗的统计推断、统计机器学习等。在Journal oftheAmerican Statistical Association (JASA), IEEE Transactions on Geoscience and Remote Sensing (TGRS), Statistica Sinica, Scandinavian Journal of Statistics, Journal of Scientific Computing等杂志发表学术论文多篇，主持多项国家自然科学基金项目。

报告摘要:Modern applications often collect data with individuals connected by a network to effectively record relationship information between individuals. In this paper, we use both covariates and the network to identify subgroup structures from a heterogeneous population, where heterogeneity arises from unknown or unobserved latent factors. We propose a penalization based method for subgroup analysis based on the median regression model, which can automatically divide the samples into subgroups by penalizing pairwise difference of intercepts for individuals connected by an edge in the network. The proposed method can also be used to predict response variables for new subjects with only covariates by taking advantage of the network reconstructed after adding these new subjects. We suggest an implementation algorithm based on the local linear approximation to the nondifferentiable and nonconvex penalty function and establish the oracle properties of the proposed estimator under some regularity conditions. Our simulation studies show that the proposed method can effectively identify heterogeneous subgroups even when the network has errors or mis-specified edges. Finally, the advantages of the proposed method are further illustrated by the analysis on a housing price data set from real estate transactions.