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 article develops 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, we design a novel distributed heterogeneity learning (DHL) method based on bivariate spline s

SUBMITTER: Yu S 

PROVIDER: S-EPMC12148239 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature