Generating spatially constrained null models for irregularly spaced data using M oran spectral randomization methods
نویسندگان
چکیده
منابع مشابه
Spectral methods to approximate the likelihood for irregularly spaced spatial data
Likelihood approaches for large irregularly spaced spatial datasets are often very difficult, if not infeasible, to use due 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) operations. We present a version of Whittle’s approximation to the Gaussian log likelihood for spatial...
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Abstract The analysis of spatial data is based on a set of assumptions, which in practice need to be checked. A commonly used assumption is that the spatial random field is second order stationary. In this paper, a test for spatial stationarity for irregularly sampled data is proposed. The test is based on a transformation of the data (a type of Fourier transform), where the correlations betwee...
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Likelihood approaches for large irregularly spaced spatial datasets are often very difficult, if not infeasible, to implement due 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. We present a version of Whittle's approximation to the Gaussian log likelihood fo...
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ژورنال
عنوان ژورنال: Methods in Ecology and Evolution
سال: 2015
ISSN: 2041-210X,2041-210X
DOI: 10.1111/2041-210x.12407