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Non-Iterative Estimation of Multiscale Geographically and Temporally Weighted Regression Model

Author

Listed:
  • Ya-Di Dai

    (Department of Statisticss, School of Mathematics and Systems Science, Xinjiang University, Urumqi 830017, China)

  • Hui-Guo Zhang

    (Department of Statisticss, School of Mathematics and Systems Science, Xinjiang University, Urumqi 830017, China)

Abstract

The Multiscale Geographically and Temporally Weighted Regression model overcomes the limitation of estimating spatiotemporal variation characteristics of regression coefficients for different variables under a single scale, making it a powerful tool for exploring the spatiotemporal scale characteristics of regression relationships. Currently, the most widely used estimation method for multiscale spatiotemporal geographically weighted models is the backfitting-based iterative approach. However, the iterative process of this method leads to a substantial computational burden and the accumulation of errors during iteration. This paper proposes a non-iterative estimation method for the MGTWR model, combining local linear fitting and two-step weighted least squares estimation techniques. Initially, a reduced bandwidth is used to fit a local linear GTWR model to obtain the initial estimates. Then, for each covariate, the optimal bandwidth and regression coefficients are estimated by substituting the initial estimates into a localized least squares problem. Simulation experiments are conducted to evaluate the performance of the proposed non-iterative method compared to traditional methods and the backfitting-based approach in terms of coefficient estimation accuracy and computational efficiency. The results demonstrate that the non-iterative estimation method for MGTWR significantly enhances computational efficiency while effectively capturing the scale effects of spatiotemporal variation in the regression coefficient functions for each predictor.

Suggested Citation

  • Ya-Di Dai & Hui-Guo Zhang, 2025. "Non-Iterative Estimation of Multiscale Geographically and Temporally Weighted Regression Model," Mathematics, MDPI, vol. 13(9), pages 1-16, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:9:p:1446-:d:1644698
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    References listed on IDEAS

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    1. Wei, Chuan-Hua & Qi, Fei, 2012. "On the estimation and testing of mixed geographically weighted regression models," Economic Modelling, Elsevier, vol. 29(6), pages 2615-2620.
    2. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
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