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Competition on Spatial Statistics for Large Datasets


As spatial datasets are becoming increasingly large and unwieldy, exact inference on spatial models becomes computationally prohibitive. Various approximation methods have been proposed to reduce the computational burden. Although comprehensive reviews on these approximation methods exist, comparisons of their performances are limited to small and medium sizes of datasets for a few selected methods. To achieve a comprehensive comparison comprising as many methods as possible, we organized the Competition on Spatial Statistics for Large Datasets. This competition had the following novel features: (1) we generated synthetic datasets with the ExaGeoStat software so that the number of generated realizations ranged from 100 thousand to 1 million; (2) we systematically designed the data-generating models to represent spatial processes with a wide range of statistical properties for both Gaussian and non-Gaussian cases; (3) the competition tasks included both estimation and prediction, and the results were assessed by multiple criteria; and (4) we have made all the datasets and competition results publicly available to serve as a benchmark for other approximation methods. In this paper, we disclose all the competition details and results along with some analysis of the competition outcomes.

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Funding was provided by King Abdullah University of Science and Technology.

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Correspondence to Marc G. Genton.

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Huang, H., Abdulah, S., Sun, Y. et al. Competition on Spatial Statistics for Large Datasets. JABES 26, 580–595 (2021).

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  • Gaussian processes
  • Matérn covariance function
  • Parameter estimation
  • Prediction
  • Tukey g-and-h random fields