报告题目：The Adaptive Projection Estimator with Enhanced Inference Efficiency
报 告 人：郑泽敏教授 中国科学技术大学
报告时间：2020年6月11日 下午 13:30-14:30
报告地点：腾讯会议 ID：473 896 994
密码: 200611
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校内联系人：赵世舜 zhaoss@jlu.edu.cn
报告摘要：
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 or thogonalization vector. This or thogonalization 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 or thogonalization 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.
报告人简介：
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。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。
