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Anisotropic matern correlation and spatial prediction using REML

Journal Article


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Abstract


  • The Mat´ern correlation function provides great flexibility for modeling spatially

    correlated random processes in two dimensions, in particular via a smoothness parameter,

    whose estimation allows data to determine the degree of smoothness of a spatial

    process. The extension to include anisotropy provides a very general and flexible class

    of spatial covariance functions that can be used in a model-based approach to geostatistics,

    in which parameter estimation is achieved via REML and prediction is within the

    E-BLUP framework. In this article we develop a general class of linear mixed models

    using an anisotropic Mat´ern class with an extended metric. The approach is illustrated by

    application to soil salinity data in a rice-growing field in Australia, and to fine-scale soil

    pH data. It is found that anisotropy is an important aspect of both datasets, emphasizing

    the value of a straightforward and accessible approach to modeling anisotropy.

Authors


  •   Haskard, K A. (external author)
  •   Cullis, Brian R.
  •   Verbyla, Ari P. (external author)

Publication Date


  • 2007

Citation


  • Haskard, K. A., Cullis, B. R. & Verbyla, A. P. (2007). Anisotropic matern correlation and spatial prediction using REML. Journal of Agricultural, Biological, and Environmental Statistics, 12 (2), 147-160.

Scopus Eid


  • 2-s2.0-34447309174

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=9422&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2087

Has Global Citation Frequency


Number Of Pages


  • 13

Start Page


  • 147

End Page


  • 160

Volume


  • 12

Issue


  • 2

Place Of Publication


  • United States

Abstract


  • The Mat´ern correlation function provides great flexibility for modeling spatially

    correlated random processes in two dimensions, in particular via a smoothness parameter,

    whose estimation allows data to determine the degree of smoothness of a spatial

    process. The extension to include anisotropy provides a very general and flexible class

    of spatial covariance functions that can be used in a model-based approach to geostatistics,

    in which parameter estimation is achieved via REML and prediction is within the

    E-BLUP framework. In this article we develop a general class of linear mixed models

    using an anisotropic Mat´ern class with an extended metric. The approach is illustrated by

    application to soil salinity data in a rice-growing field in Australia, and to fine-scale soil

    pH data. It is found that anisotropy is an important aspect of both datasets, emphasizing

    the value of a straightforward and accessible approach to modeling anisotropy.

Authors


  •   Haskard, K A. (external author)
  •   Cullis, Brian R.
  •   Verbyla, Ari P. (external author)

Publication Date


  • 2007

Citation


  • Haskard, K. A., Cullis, B. R. & Verbyla, A. P. (2007). Anisotropic matern correlation and spatial prediction using REML. Journal of Agricultural, Biological, and Environmental Statistics, 12 (2), 147-160.

Scopus Eid


  • 2-s2.0-34447309174

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=9422&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2087

Has Global Citation Frequency


Number Of Pages


  • 13

Start Page


  • 147

End Page


  • 160

Volume


  • 12

Issue


  • 2

Place Of Publication


  • United States