Correcting the Smoothing Effect of Estimators: A Spectral Postprocessor |
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| Authors: | André G Journel Phaedon C Kyriakidis and Shuguang Mao |
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| Institution: | (1) Department of Geological and Environmental Sciences, Stanford University, Stanford, California, 94305-2115;(2) Department of Geological and Environmental Sciences, Stanford University, Stanford, California, 94305-2115 |
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| Abstract: | The postprocessing algorithm introduced by Yao for imposing the spectral amplitudes of a target covariance model is shown to be efficient in correcting the smoothing effect of estimation maps, whether obtained by kriging or any other interpolation technique. As opposed to stochastic simulation, Yao's algorithm yields a unique map starting from an original, typically smooth, estimation map. Most importantly it is shown that reproduction of a covariance/semivariogram model (global accuracy) is necessarily obtained at the cost of local accuracy reduction and increase in conditional bias. When working on one location at a time, kriging remains the most accurate (in the least squared error sense) estimator. However, kriging estimates should only be listed, not mapped, since they do not reflect the correct (target) spatial autocorrelation. This mismatch in spatial autocorrelation can be corrected via stochastic simulation, or can be imposed a posteriori via Yao's algorithm. |
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| Keywords: | local accuracy conditional bias spectral amplitudes kriging stochastic simulation |
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