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Geostatistics

Journal Article


Abstract


  • Most data have a space and time label associated with them; data that are close together are usually more correlated than those that are far apart. Prediction (or forecasting) of a process at a particular label where there is no datum, from observed nearby data, is the subject of this article. One approach, known as geostatistics, is featured, from which linear methods of spatial prediction (kriging) will be considered. Brief reference is made to other linear/nonlinear, stochastic/deterministic predictors. The (linear) geostatistical method is applied to piezometric-head data around a potential nuclear-waste repository site. © 1989 American Statistical Association.

Publication Date


  • 1989

Citation


  • Cressie, N. (1989). Geostatistics. American Statistician, 43(4), 197-202. doi:10.1080/00031305.1989.10475658

Scopus Eid


  • 2-s2.0-17944373890

Web Of Science Accession Number


Start Page


  • 197

End Page


  • 202

Volume


  • 43

Issue


  • 4

Abstract


  • Most data have a space and time label associated with them; data that are close together are usually more correlated than those that are far apart. Prediction (or forecasting) of a process at a particular label where there is no datum, from observed nearby data, is the subject of this article. One approach, known as geostatistics, is featured, from which linear methods of spatial prediction (kriging) will be considered. Brief reference is made to other linear/nonlinear, stochastic/deterministic predictors. The (linear) geostatistical method is applied to piezometric-head data around a potential nuclear-waste repository site. © 1989 American Statistical Association.

Publication Date


  • 1989

Citation


  • Cressie, N. (1989). Geostatistics. American Statistician, 43(4), 197-202. doi:10.1080/00031305.1989.10475658

Scopus Eid


  • 2-s2.0-17944373890

Web Of Science Accession Number


Start Page


  • 197

End Page


  • 202

Volume


  • 43

Issue


  • 4