Covariance Tapering for Interpolation of Large Spatial Datasets

  title={Covariance Tapering for Interpolation of Large Spatial Datasets},
  author={Reinhard and Guillaume Marc and Douglas},
Interpolation of a spatially correlated random process is used in many areas. The best unbiased linear predictor, often called kriging predictor in geostatistical science, requires the solution of a large linear system based on the covariance matrix of the observations. In this article, we show that tapering the correct covariance matrix with an appropriate compactly supported covariance function reduces the computational burden significantly and still has an asymptotic optimal mean squared… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 40 references

R : A language for data analysis and graphics

  • R. Ihaka, R. Gentleman
  • Journal of Computational and Graphical Statistics
  • 1996
Highly Influential
5 Excerpts

Asymptotically efficient prediction of a random field with a misspecified covariance function

  • M L.
  • Annals of Statistics,
  • 1988
Highly Influential
7 Excerpts

Local regression models

  • M. Abramowitz, I. A. Stegun
  • 1970
Highly Influential
4 Excerpts

Geostatistical mapping with continuous moving neighborhood

  • T. M. Hamill, J. S. Whitaker, C. Snyder
  • Mathemat - ical Geology
  • 2004

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