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科学研究
RESEARCH
ROBUST SUBGROUP ANALYSIS OF HIGH- DIMENSIONAL DATA
时间  Datetime
2020-08-10 10:30 — 11:30
地点  Venue
Zoom APP(2)()
报告人  Speaker
冯兴东
单位  Affiliation
上海财经大学
邀请人  Host
王成
备注  remarks
会议号: 93052640560 会议密码: 613550
报告摘要  Abstract

Abstract: It becomes an interesting problem to identify subgroup structures in

data analysis as populations are probably heterogeneous in practice. In this pa-

per, we consider M-estimators together with both concave and pairwise fusion

penalties, which can deal with high-dimensional data containing some outliers.

The penalties are applied both on covariates and treatment effects, where the

estimation is expected to achieve both variable selection and data clustering si-

multaneously. An algorithm is proposed to process relatively large datasets based

on parallel computing. We establish the convergence analysis of the proposed al-

gorithm, the oracle property of the penalized M-estimators, and the selection

consistency of the proposed criterion. Our numerical study demonstrates that

the proposed method is promising to efficiently identify subgroups hidden in

high-dimensional data.



报告人介绍:冯兴东,上海财经大学统计与管理学院院长、统计学教授、博士生导师。研究领域为数据降维、稳健方法、分位数回归以及在经济问题中的应用、大数据统计计算等;现主持一项国家自然科学基金面上项目,已结项两项国家自然科学基金项目;已经在国际统计学顶级学术期刊发表一系列高质量论文,并应约在诸多国际会议和国外著名大学做学术讲座。