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http://dx.doi.org/10.5351/KJAS.2014.27.5.755

On the Geometric Anisotropy Inherent In Spatial Data  

Go, Hye Ji (Department of Statistics, Sungshin Women's University)
Park, Man Sik (Department of Statistics, Sungshin Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.27, no.5, 2014 , pp. 755-771 More about this Journal
Abstract
Isotropy is one of the main assumptions for the ease of spatial prediction (named kriging) based on some covariance models. A lack of isotropy (or anisotropy) in a spatial process necessitates that some additional parameters (angle and ratio) for anisotropic covariance model be obtained in order to produce a more reliable prediction. In this paper, we propose a new class of geometrically extended anisotropic covariance models expressed as a weighted average of some geometrically anisotropic models. The maximum likelihood estimation method is taken into account to estimate the parameters of our interest. We evaluate the performances of our proposal and compare it with an isotropic covariance model and a geometrically anisotropic model in simulation studies. We also employ extended geometric anisotropy to the analysis of real data.
Keywords
spatial association; isotropy; geometric anisotropy; spherical covariance model; spatial prediction; maximum likelihood estimation;
Citations & Related Records
Times Cited By KSCI : 11  (Citation Analysis)
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