Sampled Grid Pairwise Likelihood (SG-PL): An Efficient Approach for Spatial Regression on Large Data
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| Format: | Preprint |
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2025
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| _version_ | 1866916835370205184 |
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| author | Arbia, Giuseppe Nardelli, Vincenzo Salvini, Niccolo |
| author_facet | Arbia, Giuseppe Nardelli, Vincenzo Salvini, Niccolo |
| contents | Estimating spatial regression models on large, irregularly structured datasets poses significant computational hurdles. While Pairwise Likelihood (PL) methods offer a pathway to simplify these estimations, the efficient selection of informative observation pairs remains a critical challenge, particularly as data volume and complexity grow. This paper introduces the Sampled Grid Pairwise Likelihood (SG-PL) method, a novel approach that employs a grid-based sampling strategy to strategically select observation pairs. Simulation studies demonstrate SG-PL's principal advantage: a dramatic reduction in computational time -- often by orders of magnitude -- when compared to benchmark methods. This substantial acceleration is achieved with a manageable trade-off in statistical efficiency. An empirical application further validates SG-PL's practical utility. Consequently, SG-PL emerges as a highly scalable and effective tool for spatial analysis on very large datasets, offering a compelling balance where substantial gains in computational feasibility are realized for a limited cost in statistical precision, a trade-off that increasingly favors SG-PL with larger N. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_07113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sampled Grid Pairwise Likelihood (SG-PL): An Efficient Approach for Spatial Regression on Large Data Arbia, Giuseppe Nardelli, Vincenzo Salvini, Niccolo Methodology Computation Estimating spatial regression models on large, irregularly structured datasets poses significant computational hurdles. While Pairwise Likelihood (PL) methods offer a pathway to simplify these estimations, the efficient selection of informative observation pairs remains a critical challenge, particularly as data volume and complexity grow. This paper introduces the Sampled Grid Pairwise Likelihood (SG-PL) method, a novel approach that employs a grid-based sampling strategy to strategically select observation pairs. Simulation studies demonstrate SG-PL's principal advantage: a dramatic reduction in computational time -- often by orders of magnitude -- when compared to benchmark methods. This substantial acceleration is achieved with a manageable trade-off in statistical efficiency. An empirical application further validates SG-PL's practical utility. Consequently, SG-PL emerges as a highly scalable and effective tool for spatial analysis on very large datasets, offering a compelling balance where substantial gains in computational feasibility are realized for a limited cost in statistical precision, a trade-off that increasingly favors SG-PL with larger N. |
| title | Sampled Grid Pairwise Likelihood (SG-PL): An Efficient Approach for Spatial Regression on Large Data |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2507.07113 |