Covariance Tapering for Interpolation of Large Spatial Datasets

@inproceedings{Reinhard2004CovarianceTF,
  title={Covariance Tapering for Interpolation of Large Spatial Datasets},
  author={Reinhard and Guillaume Marc and Douglas},
  year={2004}
}
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
Highly Influential
This paper has highly influenced 25 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 217 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 145 extracted citations

218 Citations

0102030'06'09'12'15'18
Citations per Year
Semantic Scholar estimates that this publication has 218 citations based on the available data.

See our FAQ for additional information.

References

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

Similar Papers

Loading similar papers…