Eigenstructures of Spatial Design Matrices
In estimating the variogram of a spatial stochastic process, we use a spatial design matrix. This matrix is the key to Matheron's variogram estimator. We show how the structure of the matrix for any dimension is based on the one-dimensional spatial design matrix, and we compute explicit eigenvalues and eigenvectors for all dimensions. This design matrix involves Kronecker products of second order finite difference matrices, with cosine eigenvectors and eigenvalues. Using the eigenvalues of the spatial design matrix, the statistics of Matheron's variogram estimator are determined. Finally, a small simulation study is performed.
Year of publication: |
2002
|
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Authors: | Gorsich, David J. ; Genton, Marc G. ; Strang, Gilbert |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 80.2002, 1, p. 138-165
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Publisher: |
Elsevier |
Keywords: | discrete cosine transform eigenvalue eigenvector kriging Kronecker product Matheron's estimator variogram spatial statistics |
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