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科学研究
RESEARCH
The Adaptive Projection Estimator with Enhanced Inference Efficiency
时间  Datetime
2020-08-14 14:00 — 15:00
地点  Venue
Zoom APP(2)()
报告人  Speaker
郑泽敏
单位  Affiliation
中国科学技术大学
邀请人  Host
统计系
备注  remarks
会议id: 99584149157 密码: 149153
报告摘要  Abstract

Abstract:As a popular class of methods, inference via the de-biased estimators typically requires a large sample size to guarantee the asymptotic normality and allows a relatively small number of nonzero coefficients above the identifiable level. To alleviate such constraints and enhance the inference efficiency, we develop a new inference procedure via an adaptive projection estimator, which is based on the adaptive orthogonalization vector. This orthogonalization vector is adaptive in that it is orthogonal to the other covariate vectors corresponding to the identifiable coefficients, and at the same time being a relaxed orthogonalization against the remaining unidentifiable covariates. In this way, it completely removes the impacts of identifiable coefficients and controls that of the unidentifiable ones at a neglectable level, yielding much weaker constraint on both the sample size and the number of nonzero coefficients.




报告人介绍:郑泽敏,男,现为中国科学技术大学管理学院教授、统计与金融系主任、博士生导师,其研究方向是高维统计推断和大数据问题。郑泽敏博士在横跨这一领域的若干关键研究课题上取得了富有创造性的研究成果,研究成果发表在Journal of the Royal Statistical Society: Series B(JRSSB)、Operations Research(OR)、Annals of Statistics(AOS)、Journal of Machine Learning Research(JMLR)等国际统计学、机器学习及管理优化顶级期刊上,曾获南加州大学授予的优秀科研奖和美国数理统计协会颁发的科研新人奖,并于2017年入选第十三批中组部‘千人计划’青年项目。