学术报告:2021年 07月12日 14:00-15:00,复旦大学-高凤楠

发布者:吕小俊发布时间:2021-07-11浏览次数:13

时间:2021年7月12日14:00-15:00


地点:数学学院第一报告厅


报告人:高凤楠,复旦大学大数据学院青年副研究员。2017年荷兰莱顿大学师从著名统计学家Aad van der Vaart并取得博士学位。2016年9月之后在由复旦大学大数据学院及上海数学中心合聘并开始任教。主要研究领域包括非参数贝叶斯统计、网络科学中的高维统计推论、复杂网络中的概率方法、社交网络建模及分析。已经在阿姆斯特丹、上海、多伦多、英国剑桥、荷兰埃因霍芬、伦敦、新泽西、新加坡、台北等多地做学术报告,且在第八届世界华人数学家大会上做45分钟报告。


Title: Two-sample testing of high-dimensional linear regression coefficients via complementary sketching
abstract: We introduce a new method for two-sample testing of high-dimensional linear regression coefficients without assuming that those coefficients are individually estimable. The procedure works by first projecting the matrices of covariates and response vectors along directions that are complementary in sign in a subset of the coordinates, a process which we call `complementary sketching'. The resulting projected covariates and responses are aggregated to form two test statistics, which are shown to have essentially optimal asymptotic power under a Gaussian design when the difference between the two regression coefficients is sparse and dense respectively. Simulations confirm that our methods perform well in a broad class of settings.