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- W4235587005 abstract "Abstract Spectral methods are a powerful tool for characterizing and modeling spatial dependence, while offering significant computational advantages. Using the spectral representation of a spatial process, we can easily construct valid (positive definite) covariance functions and introduce new models to characterize complex dependence structures of spatial processes. Likelihood approaches for very large spatial datasets are often very difficult, if not infeasible, to implement owing to computational limitations. Even when we can assume normality, exact calculations of the likelihood for a Gaussian spatial process observed at n locations requires O( n 3 ) operations. The spectral version of the Gaussian log‐likelihood for gridded data requires O( n log2 n ) operations and does not involve calculating determinants." @default.
- W4235587005 created "2022-05-12" @default.
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- W4235587005 date "2012-08-31" @default.
- W4235587005 modified "2023-10-04" @default.
- W4235587005 title "Spectral Methods" @default.
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- W4235587005 doi "https://doi.org/10.1002/9780470057339.vnn060" @default.
- W4235587005 hasPublicationYear "2012" @default.
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