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<p>Estimating spatial regression models on large, irregularly structured data networks poses significant computational hurdles. The Pairwise Likelihood (PL) proposed by Arbia (2014) offers a pathway to simplify the estimations by selecting a subset of pairs of observations, but the process of selection of the pairs remains a critical challenge, particularly when the data volume and complexity grow. This paper introduces a novel approach that employs a grid-based sampling strategy to strategically select observation pairs. Our simulation studies show clearly the new method’s main advantage consisting of a dramatic reduction in computational time—often by orders of magnitude—when compared to other benchmark methods. This substantial acceleration is achieved with an acceptable trade-off of statistical efficiency. An empirical application further validates the practical utility of the proposed method. As a consequence, the proposed method emerges as a highly scalable and effective tool for spatial and network analysis on very large datasets, offering a compelling balance where substantial gains in computational feasibility are realized for a limited cost in statistical precision.</p>

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Sampled Grid Pairwise Likelihood (SG-PL): An Efficient Approach for Spatial Regression on Large Data Networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月3日

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  • Giuseppe Arbia,
  • Vincenzo Nardelli,
  • Niccolò Salvini

摘要

Estimating spatial regression models on large, irregularly structured data networks poses significant computational hurdles. The Pairwise Likelihood (PL) proposed by Arbia (2014) offers a pathway to simplify the estimations by selecting a subset of pairs of observations, but the process of selection of the pairs remains a critical challenge, particularly when the data volume and complexity grow. This paper introduces a novel approach that employs a grid-based sampling strategy to strategically select observation pairs. Our simulation studies show clearly the new method’s main advantage consisting of a dramatic reduction in computational time—often by orders of magnitude—when compared to other benchmark methods. This substantial acceleration is achieved with an acceptable trade-off of statistical efficiency. An empirical application further validates the practical utility of the proposed method. As a consequence, the proposed method emerges as a highly scalable and effective tool for spatial and network analysis on very large datasets, offering a compelling balance where substantial gains in computational feasibility are realized for a limited cost in statistical precision.