Sub‐Model Aggregation for Scalable Eigenvector Spatial Filtering: Application to Spatially Varying Coefficient Modeling

DOI Web Site 被引用文献1件 参考文献57件
  • Daisuke Murakami
    Department of Statistical Data Science Institute for Statistical Mathematics Tachikawa Japan
  • Shonosuke Sugasawa
    Graduate School of Economics Keio University Tokyo Japan
  • Hajime Seya
    Department of Civil Engineering, Graduate School of Engineering Kobe University Kobe Japan
  • Daniel A. Griffith
    School of Economic, Political and Policy Sciences The University of Texas at Dallas Richardson Texas USA

書誌事項

公開日
2024-02-29
資源種別
journal article
権利情報
  • http://creativecommons.org/licenses/by/4.0/
DOI
  • 10.1111/gean.12393
公開者
Wiley

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説明

<jats:p>This study proposes a method for aggregating/synthesizing global and local sub‐models for fast and flexible spatial regression modeling. Eigenvector spatial filtering (ESF) was used to model spatially varying coefficients and spatial dependence in the residuals by sub‐model, while the generalized product‐of‐experts method was used to aggregate these sub‐models. The major advantages of the proposed method are as follows: (i) it is highly scalable for large samples in terms of accuracy and computational efficiency; (ii) it is easily implemented by estimating sub‐models independently first and aggregating/averaging them thereafter; and (iii) likelihood‐based inference is available because the marginal likelihood is available in closed‐form. The accuracy and computational efficiency of the proposed method are confirmed using Monte Carlo simulation experiments. This method was then applied to residential land price analysis in Japan. The results demonstrate the usefulness of this method for improving the interpretability of spatially varying coefficients. The proposed method is implemented in an R package spmoran.</jats:p>

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