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Principles for statistical inference on big spatio-temporal data from climate models

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  • Castruccio, Stefano
  • Genton, Marc G.

Abstract

The vast increase in size of modern spatio-temporal data sets has prompted statisticians working in environmental applications to develop new and efficient methodologies that are still able to achieve inference for nontrivial models within an affordable time. Climate model outputs push the limits of inference for Gaussian processes, as their size can easily be larger than 10 billion data points. Drawing from our experience in a set of previous work, we provide three principles for the statistical analysis of such large data sets that leverage recent methodological and computational advances. These principles emphasize the need of embedding distributed and parallel computing in the inferential process.

Suggested Citation

  • Castruccio, Stefano & Genton, Marc G., 2018. "Principles for statistical inference on big spatio-temporal data from climate models," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 92-96.
    • Handle: RePEc:eee:stapro:v:136:y:2018:i:c:p:92-96
    DOI: 10.1016/j.spl.2018.02.026
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    References listed on IDEAS

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    1. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
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    Cited by:

    1. Marc G. Genton & Ying Sun, 2019. "Comments on: Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 338-341, June.
    2. Reid, Nancy, 2018. "Statistical science in the world of big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 42-45.
    3. Ying C. MacNab, 2018. "Rejoinder on: Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 554-569, September.
    4. Edwards, Matthew & Castruccio, Stefano & Hammerling, Dorit, 2020. "Marginally parameterized spatio-temporal models and stepwise maximum likelihood estimation," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    5. Cao, Jian & Genton, Marc G. & Keyes, David E. & Turkiyyah, George M., 2021. "Sum of Kronecker products representation and its Cholesky factorization for spatial covariance matrices from large grids," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    6. Huang Huang & Stefano Castruccio & Allison H. Baker & Marc G. Genton, 2023. "Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 324-344, June.

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