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Modeling the Semivariogram: New Approach, Methods Comparison, and Simulation Study
A. Gribov,1 K. Krivoruchko,2 J. M. Ver Hoef3
1Environmental Systems Research Institute Redlands, California, U.S.A.
2Environmental Systems Research Institute Redlands, California, U.S.A.
2Alaska Department of Fish and Game Fairbanks, Alaska, U.S.A.
This chapter proposes some new methods for computing empirical semivariograms and covariances and for fitting semivariogram and covariance models to empirical data. Grid-based empirical semivariograms and covariances are described, in which the grid values are smoothed using triangular kernels. A model-fitting procedure using modified iterative weighted least squares is presented. This algorithm is shown to be reliable for a wide range of data types and conditions, and its implementation in commercial software is discussed. Comparisons to restricted maximum likelihood estimation are also discussed.
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