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Functional varying-coefficient mixed-effects autoregressive model for spatiotemporal data with application to wind speed

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  • Liang, Shiting
  • Wei, Honglei
  • Zheng, Haitao

Abstract

Modeling spatiotemporal data with complex spatial heterogeneity presents major challenges in scientific and engineering fields. In this study, a semiparametric mixed-effects autoregressive model incorporating spatially varying coefficients is proposed to capture such complexities. A unified joint estimation framework based on a generalized Expectation-Maximization (GEM) algorithm is developed to simultaneously estimate fixed effects and random components. Under some regularity conditions, consistency of the estimators is established. Simulation results demonstrate the method’s robustness and stability, especially in small-sample or weak-signal settings. An application to wind speed data from wind farms illustrates the model’s effectiveness in revealing intricate spatiotemporal patterns and its practical utility for environmental and energy system analysis.

Suggested Citation

  • Liang, Shiting & Wei, Honglei & Zheng, Haitao, 2026. "Functional varying-coefficient mixed-effects autoregressive model for spatiotemporal data with application to wind speed," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 249(C), pages 871-885.
    • Handle: RePEc:eee:matcom:v:249:y:2026:i:c:p:871-885
    DOI: 10.1016/j.matcom.2026.05.022
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