报告题目: A Distributed Approach for Learning Spatial Heterogeneity

报告人:朱正元(爱荷华州立大学)

报告时间:2023年6月29日15:30-16:30

报告地点:文波楼201教室

摘要:Spatial regression is widely used for modeling the relationship between a spatial dependent variable and explanatory covariates. In many applications there are spatial heterogeneity in such relationships, i.e., the regression coefficients may vary across space.It is a fundamental and challenging problem to detect the systematic variation in the model and determine which locations share common regression coefficients and where the boundary is.In this talk, we introduce a Spatial Heterogeneity Automatic Detection and Estimation (SHADE) procedure for automatically and simultaneously subgrouping and estimating covariate effects for spatial regression models, and present adistributed spanning-tree-based fused-lasso regression (DTFLR) approach to learn spatial heterogeneity in the distributed network systems, where the data are locally collected and held by nodes. To solve the problem parallelly, we design a distributed generalized alternating direction method of multiplier algorithm, which has a simple node-based implementation scheme and enjoys a linear convergence rate. Theoretical and numerical results as well as real-world data analysis will be presented to show that our approach outperforms existing works in terms of estimation accuracy, computation speed, and communication costs.

主讲人简介：

       Dr. Zhengyuan Zhu is the College of Liberal Arts and Sciences Dean's Professor, Director of the Center for Survey Statistics Methodology, and Professor of Statistics in the Department of Statistics at Iowa State University. He received his B.S. in Mathematics from Fudan University and Ph.D. in Statistics from the University of Chicago. His research interests include spatial statistics, survey statistics, machine learning, statistical data integration, and applications in environmental science, agriculture, remote sensing, and official statistics. He is a fellow of the American Statistics Association, and an elected member of the International Statistical Institute.