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Reduced Basis Kriging for Big Spatial Fields

Author

Listed:
  • Karl Pazdernik

    (Pacific Northwest National Laboratory)

  • Ranjan Maitra

    (Iowa State University)

  • Douglas Nychka

    (Colorado School of Mines)

  • Stephan Sain

    (Jupiter Intelligence)

Abstract

In spatial statistics, a common method for prediction over a Gaussian random field (GRF) is maximum likelihood estimation combined with kriging. For massive data sets, kriging is computationally intensive, both in terms of CPU time and memory, and so fixed rank kriging has been proposed as a solution. The method however still involves operations on large matrices, so we develop an alteration to this method by utilizing the approximations made in fixed rank kriging combined with restricted maximum likelihood estimation and sparse matrix methodology. Experiments show that our methodology can provide additional gains in computational efficiency over fixed-rank kriging without loss of accuracy in prediction. The methodology is applied to climate data archived by the United States National Climate Data Center, with very good results.

Suggested Citation

  • Karl Pazdernik & Ranjan Maitra & Douglas Nychka & Stephan Sain, 2018. "Reduced Basis Kriging for Big Spatial Fields," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(2), pages 280-300, August.
    • Handle: RePEc:spr:sankha:v:80:y:2018:i:2:d:10.1007_s13171-018-0129-7
    DOI: 10.1007/s13171-018-0129-7
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    References listed on IDEAS

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