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The variance-based cross-variogram between two spatial processes, Z1
(·) and Z2
(·), is var (Z1
(
u
) – Z2
(
v
)), expressed generally as a bivariate function of spatial locations
uandv. It characterizes the cross-spatial dependence between Z1
(·) and Z2
(·) and can be used to obtain optimal multivariable predictors (cokriging). It has also been called the pseudo cross-variogram; here we compare its properties to that of the traditional (covariance-based) cross-variogram, cov (Z1
(
u
) – Z1
(
v
), Z2
(
u
) – Z2
(
v
)). One concern with the variance-based cross-variogram has been that Z1
(·) and Z2
(·) might be measured in different units (apples and oranges). In this note, we show that the cokriging predictor based on variance-based cross-variograms can handle any units used for Z1
(·) and Z2
(·); recommendations are given for an appropriate choice of units. We review the differences between the variance-based cross-variogram and the covariance-based cross-variogram and conclude that the former is more appropriate for cokriging. In practice, one often assumes that variograms and cross-variograms are functions of
uandv
only through the difference
u – v. This restricts the types of models that might be fitted to measures of cross-spatial dependence. 相似文献
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