报告题目：High-dimensional Statistics and Balanced Estimation for Measurement Error Models

报告时间：北京时间2020年10月10日19:00-21:00

报告地点：腾讯会议  会议 ID：574 545 186

摘要：Noisy and missing data are often encountered in real applications such that the observed covariates contain measurement errors. Despite the rapid progress of model selection with contaminated covariates in high dimensions, methodology that enjoys virtues in all aspects of prediction, variable selection, and computation remains largely unexplored. In this paper, we propose a new method called as the balanced estimation for high-dimensional error-in-variables regression to achieve an ideal balance between prediction and variable selection under both additive and multiplicative measurement errors. It combines the strengths of the nearest positive semi-definite projection and the combined $L_1$ and concave regularization, and thus can be efficiently solved through the coordinate optimization algorithm. We also provide theoretical guarantees for the proposed methodology by establishing the oracle prediction and estimation error bounds equivalent to those for  Lasso with the clean data set, as well as an explicit and asymptotically vanishing bound on the false sign rate that controls overfitting, a serious problem under measurement errors. Our numerical studies show that the amelioration of variable selection will in turn improve the prediction and estimation performance under measurement errors.

报告人简介：北京师范大学统计学院教授，博士生导师。全国工业统计学教学研究会常务理事、中国概率统计学会第十一届理事、北京应用统计学会常务理事、中国现场统计研究会高维数据统计分会理事、生存分析分会理事和副秘书长、北京大数据协会理事和美国数学评论评论员。主要研究方向是非参数统计、高维统计、统计学习、纵向数据、测量误差数据和因果推断等。迄今为止，在Annals of Statistics, Journal of the American Statistical Association, Statistics and Computing, Statistica Sinica，中国科学：数学，和统计研究等学术期刊上发表学术论文90余篇。在科学出版社出版专著《纵向数据半参数模型》和《现代测量误差模型》，后者入选《现代数学基础丛书》系列。入选北京市属高等学校人才强教深化计划“中青年骨干人才培养计划”，北京市优秀人才培养资助计划和北京工业大学“京华人才”支持计划。主持国家自然科学基金、北京市自然科学基金和北京市教委科技计划面上项目等国家和省部级科研项目10余项。