Spatiotemporal Heterogeneity Learning: Generalized SpatioTemporal Semi-Varying Coefficient Models With Structure Identification.

ABSTRACT: This paper proposes a class of Generalized SpatioTemporal Semi-Varying Coefficient Models (GST-SVCMs) with structure identification to enhance the detection and interpretation of spatiotemporal heterogeneity in factors influencing response variables. The proposed framework effectively distinguishes between spatiotemporally varying and constant effects, addressing a key limitation of current modeling approaches. By identifying and separating these components, the GST-SVCM structure identification method improves both computational efficiency and the statistical power of downstream analyses. The estimators of constant coefficients and varying coefficient functions are consistent, and the estimators of the constant coefficients are asymptotically normal, facilitating reliable statistical infe

SUBMITTER: Gu Z 

PROVIDER: S-EPMC12614080 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature