A Distributed Approach for Learning Spatial Heterogeneity

Fri, 28 October, 2022 11:00am - 12:00pm

Speaker: Zhengyuan Zhu, Iowa State University

Abstract: Spatial regression is widely used for modeling the relationship between a spatial dependent variable and explanatory covariates. In many applications there is 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 a distributed 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.

Where
Media and Public Affairs Building 805 21st Street, NW Washington DC 20052
Room: 309

Admission
Open to everyone.

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