Mathematics Date Science Seminar: Shan Yu,Distributed Heterogeneity Learning for Generalized Partial
This event is in the past.
Speaker: Shan Yu, Department of Statistics, University of Virginia
Time: Wednesday, February 7, 2:30pm-3:30pm
Place: Virtual
Zoom link:
https://wayne-edu.zoom.us/j/96316494795?pwd=Ylc3M0R0R1BYaUZGSnB2dkI2UFRVQT09
Meeting ID: 963 1649 4795
Passcode: 271178
Title: Distributed Heterogeneity Learning for Generalized Partially Linear Models with Spatially Varying Coefficients
Abstract: Spatial heterogeneity is of great importance in social, economic, and environmental science studies. The spatially varying coefficient model is a popular and effective spatial regression technique to address spatial heterogeneity. However, accounting for heterogeneity comes at the cost of reducing model parsimony. To balance flexibility and parsimony, this talk will present a class of generalized partially linear spatially varying coefficient models which allow the inclusion of both constant and spatially varying effects of covariates. Another significant challenge in many applications comes from the enormous size of the spatial datasets collected from modern technologies. To tackle this challenge, this talk introduces a novel distributed heterogeneity learning (DHL) method based on bivariate spline smoothing over a triangulation of the domain. The proposed DHL algorithm has a simple, scalable, and communication-efficient implementation scheme that can almost achieve linear speedup. In addition, we provide rigorous theoretical support for the DHL framework. The proposed DHL method is evaluated through extensive simulation studies and analyses of the U.S. loan application data.