Nonparametric estimation of the variogram and its spectrum
In the study of intrinsically stationary spatial processes, a new nonparametric variogram estimator is proposed through its spectral representation. The methodology is based on estimation of the variogram's spectrum by solving a regularized inverse problem through quadratic programming. The estimated variogram is guaranteed to be conditionally negative-definite. Simulation shows that our estimator is flexible and generally has smaller mean integrated squared error than the parametric estimator under model misspecification. Our methodology is applied to a spatial dataset of decadal temperature changes. Copyright 2011, Oxford University Press.
Year of publication: |
2011
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Authors: | Huang, Chunfeng ; Hsing, Tailen ; Cressie, Noel |
Published in: |
Biometrika. - Biometrika Trust, ISSN 0006-3444. - Vol. 98.2011, 4, p. 775-789
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Publisher: |
Biometrika Trust |
Saved in:
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