Click on the DOI link to access the article for free at the publisher's website.There are many reasons for the popular use of the isotropic or geometrically anisotropic covariance function and variogram in spatial statistics. A less known reason demonstrated in this paper is that an isotropic or geometrically anisotropic model would be the only choice in certain circumstances, for instance, when the underlying random field is smooth enough.Peer reviewe
Paper deals with a problem of testing isotropy against geometric anisotropy for Gaussian spatial dat...
We develop a new methodology for estimating and testing the form of anisotropy of homogeneous spatia...
Geostatistical analysis of soil properties is undertaken to allow prediction of values of these prop...
Stationarity in space presents two aspects: homogeneity and isotropy. They correspond respectively ...
A common requirement for spatial analysis is the modeling of the second-order structure. While the a...
An important step in modeling spatially-referenced data is appropriately specifying the second order...
Isotropy of a point process, defined as invariance of the distribution under rotation, is often assu...
This paper investigates the use of non-Euclidean distances to characterize isotropic spatial depende...
For modeling spatial processes, we propose rich classes of range anisotropic covariance structures t...
Summarization: Spatially referenced data often have autocovariance functions with elliptical isoleve...
Click on the DOI link to access the article (may not be free).An isotropic scalar or vector random f...
AbstractIn this paper an exploratory technique based on the diagonalization of cross-variogram matri...
Extreme values geostatistics make it possible to model the asymptotic behaviors of random phenomena ...
This work addresses the question of building useful and valid models of anisotropic variograms for s...
This paper represents a survey of recent advances in modeling of space or space-time Gaussian Random...
Paper deals with a problem of testing isotropy against geometric anisotropy for Gaussian spatial dat...
We develop a new methodology for estimating and testing the form of anisotropy of homogeneous spatia...
Geostatistical analysis of soil properties is undertaken to allow prediction of values of these prop...
Stationarity in space presents two aspects: homogeneity and isotropy. They correspond respectively ...
A common requirement for spatial analysis is the modeling of the second-order structure. While the a...
An important step in modeling spatially-referenced data is appropriately specifying the second order...
Isotropy of a point process, defined as invariance of the distribution under rotation, is often assu...
This paper investigates the use of non-Euclidean distances to characterize isotropic spatial depende...
For modeling spatial processes, we propose rich classes of range anisotropic covariance structures t...
Summarization: Spatially referenced data often have autocovariance functions with elliptical isoleve...
Click on the DOI link to access the article (may not be free).An isotropic scalar or vector random f...
AbstractIn this paper an exploratory technique based on the diagonalization of cross-variogram matri...
Extreme values geostatistics make it possible to model the asymptotic behaviors of random phenomena ...
This work addresses the question of building useful and valid models of anisotropic variograms for s...
This paper represents a survey of recent advances in modeling of space or space-time Gaussian Random...
Paper deals with a problem of testing isotropy against geometric anisotropy for Gaussian spatial dat...
We develop a new methodology for estimating and testing the form of anisotropy of homogeneous spatia...
Geostatistical analysis of soil properties is undertaken to allow prediction of values of these prop...