 Functional varying-coefficient mixed-effects autoregressive model for spatiotemporal data with application to wind speedShiting Liang, Honglei Wei and Haitao Zheng Mathematics and Computers in Simulation (MATCOM), 2026, vol. 249, issue C, 871-885 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. Keywords: Spatiotemporal modeling; Functional data analysis; Spatially varying coefficients; GEM algorithm; Wind energy applications (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:249:y:2026:i:c:p:871-885 DOI: 10.1016/j.matcom.2026.05.022 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
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