题目：Structure-Adaptive Sequential Testing for Online False Discovery Rate Control

主讲人：浙江大学 孙文光教授

主持人：统计学院 常晋源教授

时间：2022年5月31日（周二）上午10:00-11:00

地点：腾讯会议，432 396 004

报告摘要：

This talk considers the online testing of a stream of hypotheses. A real–time decision must be made before the next data point arrives. The error rate is required to be controlled at all decision points. Conventional simultaneous testing rules are no longer applicable due to the more stringent error constraints and absence of future data. Moreover, the online decision–making process may come to a halt when the total error budget, or alpha–wealth, is exhausted. This work develops a new class of structure–adaptive sequential testing (SAST) rules for online false discovery rate (FDR) control. A key element in our proposal is a new alpha–investing algorithm that precisely characterizes the gains and losses in sequential decision making. SAST captures time varying structures of the data stream, learns the optimal threshold adaptively in an ongoing manner and optimizes the alpha-wealth al across different time periods. We present theory and numerical results to show that SAST is asymptotically valid for online FDR control and achieves substantial power gain over existing online testing rules. This is the joint work with Bowen Gang from Fudan University and Weinan Wang from SNAP, Inc.

主讲人简介：

孙文光，浙江大学求是特聘教授、博士生导师。现任浙江大学数据科学研究中心主任。

2008年博士毕业于宾夕法尼亚大学，师从著名统计学家、COPSS奖取得者蔡天文教授。主要研究方向为大范围多重假设检验，选择性推断，经验贝叶斯方法，迁移学习，机器学习的公平性，统计决策理论。

回国前任美国南加利福尼亚大学马绍尔商学院终身正教授，数据科学与运筹学系博士培养项目主任（主管统计，运筹和信息系统三个学科），马绍尔商学院全日制工商管理项目教学主要负责人。曾获美国国家科学基金会杰出青年教授奖(CAREER AWARD)，南加大马绍尔商学院杰出研究奖。2018年在英国皇家统计学会期刊发表讨论文章（Discussion Paper）并受邀到英国皇家统计学会做报告。担任JRSS-B（英国皇家统计会刊）以及Journal of Multivariate Analysis（多元统计分析）的副主编。四次作为主要负责人取得美国国家科学研究基金 。