报告题目：Partially-Global Fréchet Regression
报告嘉宾：吴义超 伊利诺伊大学芝加哥分校
报告时间：2024年6月13日(周四) 10:00-11:30
报告地点：中国科学院大学中关村校区教学楼N215
内容摘要
We propose a partially-global Fréchet regression model by extending the profiling technique for the partially linear regression model (Severini and Wong 1992). This extension allows for the response to come from a generic metric space and can incorporate a combination of Euclidean predictors and a predictor which comes from another generic metric space. By melding together the local and global Fréchet regression models proposed by Petersen and Müller (2019), we gain a model that is more flexible than global Fréchet regression and more accurate than local Fréchet regression when the data generating process relies on a non-Euclidean predictor or is truly “global (linear)” for some scalar predictors. In this paper, we provide theoretical support for partially-global Fréchet regression and demonstrate its competitive finite-sample performance when applied to both simulated data and to real data which is too complex for traditional statistical methods.
主讲人简介
Yichao Wu is a Professor of Statistics at the University of Illinois at Chicago. He received his Ph.D. in Statistics from The University of North Carolina at Chapel Hill in May 2006. And joined The University of Illinois at Chicago in August 2017.
