Large-Scale Inference On Heterogeneous Data: Selection Bias, Multiple Testing, and Invidious Comparisons
报告人： Wenguang Sun (Zhejiang University)
时间：2022-12-15 10:00-11:00
地点： Tencent Meeting (436-439-849)
Abstract: The simultaneous inference of many parameters based on data collected from corresponding units is a key research problem that has received much attention. Effectively pooling information across samples while correctly accounting for heterogeneity presents a significant challenge. In this talk, I discuss a unified framework to address three fundamental issues in large-scale inference under heteroscedasticity: aggravated selection bias in estimation, unexpected efficiency loss in standardization, and invidious comparisons in ranking and selection.
About the Speaker:
孙文光是浙江大学求是讲席教授和数据科学研究中心主任。国家高层次人才。2003年大阳城2138学士，2008年宾夕法尼亚大学博士。回国前担任美国南加州大学（USC）马绍尔商学院数据科学与运筹系教授。主要研究方向为大规模统计推断、整合分析和迁移学习、共形预测、选择性推断和统计决策理论。曾获美国科学基金会CAREER Award，USC商学院杰出研究奖和Golden Apple最佳教学奖等, 曾担任JRSS-B及Journal of Multivariate Analysis的副主编。
Online: Tencent Meeting（ID: 436-439-849）
Meeting Link: https://meeting.tencent.com/dm/WwJOpWgGciie
