报告题目：Distributed Estimation Framework via Componentwise Debiasing

报告人：夏志明教授（西北大学）

报告时间：2024年6月20日（周四）下午15: 00 -16:00

报告地点：数学与统计学院235报告厅

报告摘要:

In this talk, we will discuss a new distributed estimation framework via componentwise debiasing, especially focusing on feature screening in processing ultra-high-dimensional data. When the sample size N and the number of features p are both large, the implementation of classic screening methods can be numerically challenging. In this talk, we propose a distributed estimation framework for big data setup. In the spirit of“divide-and-conquer”, the proposed framework expresses a correlation measure as a function of several component parameters, each of which can be distributively estimated using a natural U-statistic from data segments. With the component estimates aggregated, we obtain final correlation estimate that can be readily used for screening features. This framework enables distributed storage and parallel computing and thus is computationally attractive. Due to the unbiased distributive estimation of the component parameters, the final aggregated estimate achieves a high accuracy that is insensitive to the number of data segments m. Under mild conditions, we show that the aggregated correlation estimator is as efficient as the centralized estimator in terms of the probability convergence bound and the mean squared error rate; the corresponding screening procedure enjoys sure screening property for a wide range of correlation measures. The promising performances of the new method are supported by extensive numerical examples.

报告人简介：夏志明，教授，博士生导师，西北大学数学学院副院长，陕西省数学会学术交流工作委员会副主任，中国现场统计研究会多元分析应用专业委员会常务理事、教育统计与管理分会常务理事。主要致力于张量数据分析、大数据异质性结构推断、分布式统计推断与计算、生物统计学等数据科学理论与应用研究。在“Biometrika”,“Technometrics”、“IEEE Transaction on Cybernetics”、“Journal of Machine Learning Research”、“Statistics in Medicine”等国际统计与机器学习期刊以及“中国科学”等国内期刊发表论文50余篇；主持国家自然科学基金项目4项，主持省部级项目3项,作为骨干成员获得“陕西省科学技术进步奖”二、三等奖共2项，“陕西省高校科学技术奖”一等奖共2项，“陕西省国防科技进步奖”一等奖1项；先后赴香港科技大学、佛罗里达大学等科研机构进行专业访问与学术交流。