“2021首师大青年统计论坛”系列报告

题目：Large-Scale Inference Via The Adaptive Projection Estimator

报告人：郑泽敏（中国科学技术大学） 

时间：2021年4月29日周四晚上19:00-20:00

地点：线上腾讯会议（会议号：709 455 5647）

Abstract : Large-scale inference via de-biasing or relaxed orthogonalization has gained increasing popularity in high dimensions. Despite their usefulness, existing methods typically allow a much smaller number of nonzero coefficients compared to that in consistent estimation. To alleviate such constraint and take full advantage of the identifiable predictors, we develop a new inference procedure via the adaptive projection estimator (APE). The construction of APE is based on an adaptive orthogonalization vector which is strictly orthogonal to the identifiable covariate vectors, and at the same time being a relaxed orthogonalization against the remaining unidentifiable ones. In this way, it eliminates the bias induced by the estimation errors of identifiable coefficients and substantially enhances the inference efficiency. Furthermore, by considering approximately sparse models mixed of identifiable and weak signals, we establish comprehensive theoretical guarantees for valid inference via APE. In particular, after exploring partially penalized and penalized regressions separately, two complementary theoretical bounds are provided, showing that the maximum number of nonzero identifiable coefficients can be at least as large as that for consistent estimation. The usefulness of the proposed method is demonstrated through simulations and the stock short interest and aggregate return data set.

报告人简介：郑泽敏，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）、Annals of Statistics（AOS）、Operations Research（OR）、Journal of Machine Learning Research（JMLR）、Journal of Business & Economic Statistics （JBES）等国际统计学、机器学习、计量经济学及管理优化领域的顶级期刊上，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年入选中组部青年创新人才计划。

联系人：周洁、胡涛

举办单位：565net必赢客户端统计系 、北京应用统计学会、 交叉科学研究院