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Competition on Spatial Statistics for Large Datasets

Author

Listed:
  • Huang Huang

    (King Abdullah University of Science and Technology (KAUST))

  • Sameh Abdulah

    (King Abdullah University of Science and Technology (KAUST))

  • Ying Sun

    (King Abdullah University of Science and Technology (KAUST))

  • Hatem Ltaief

    (King Abdullah University of Science and Technology (KAUST))

  • David E. Keyes

    (King Abdullah University of Science and Technology (KAUST))

  • Marc G. Genton

    (King Abdullah University of Science and Technology (KAUST))

Abstract

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.

Suggested Citation

  • Huang Huang & Sameh Abdulah & Ying Sun & Hatem Ltaief & David E. Keyes & Marc G. Genton, 2021. "Competition on Spatial Statistics for Large Datasets," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 580-595, December.
    • Handle: RePEc:spr:jagbes:v:26:y:2021:i:4:d:10.1007_s13253-021-00457-z
    DOI: 10.1007/s13253-021-00457-z
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    References listed on IDEAS

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    1. Abhirup Datta & Sudipto Banerjee & Andrew O. Finley & Alan E. Gelfand, 2016. "Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 800-812, April.
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    3. Litvinenko, Alexander & Sun, Ying & Genton, Marc G. & Keyes, David E., 2019. "Likelihood approximation with hierarchical matrices for large spatial datasets," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 115-132.
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    6. Matthias Katzfuss, 2017. "A Multi-Resolution Approximation for Massive Spatial Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 201-214, January.
    7. Ganggang Xu & Marc G. Genton, 2017. "Tukey -and- Random Fields," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1236-1249, July.
    8. Matthew J. Heaton & Abhirup Datta & Andrew O. Finley & Reinhard Furrer & Joseph Guinness & Rajarshi Guhaniyogi & Florian Gerber & Robert B. Gramacy & Dorit Hammerling & Matthias Katzfuss & Finn Lindgr, 2019. "A Case Study Competition Among Methods for Analyzing Large Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 398-425, September.
    9. Kaufman, Cari G. & Schervish, Mark J. & Nychka, Douglas W., 2008. "Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1545-1555.
    10. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
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    Cited by:

    1. Roman Flury & Reinhard Furrer, 2021. "Discussion on Competition for Spatial Statistics for Large Datasets," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 599-603, December.
    2. Wanfang Chen & Marc G. Genton, 2023. "Are You All Normal? It Depends!," International Statistical Review, International Statistical Institute, vol. 91(1), pages 114-139, April.
    3. Denis Allard & Lucia Clarotto & Thomas Opitz & Thomas Romary, 2021. "Discussion on “Competition on Spatial Statistics for Large Datasets”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 604-611, December.
    4. Quan Vu & Yi Cao & Josh Jacobson & Alan R. Pearse & Andrew Zammit-Mangion, 2021. "Discussion on “Competition on Spatial Statistics for Large Datasets”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 614-618, December.
    5. Huang Huang & Sameh Abdulah & Ying Sun & Hatem Ltaief & David E. Keyes & Marc G. Genton, 2021. "Rejoinder," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 621-623, December.

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