Importance of Orthogonalization Algorithm in Modeling Conditional Distributions by Orthogonal Transformed Indicator Methods |
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Authors: | A. E. Tercan |
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Affiliation: | (1) Department of Mining Engineering, Hacettepe University, Beytepe, Ankara, 06532, Turkey |
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Abstract: | The orthogonal transformed indicator approach to conditional cumulative distribution functions is based on the decomposition of the indicator variogram matrix as a matrix product. This paper explores the manner in which the decomposition algorithm affects the conditional cumulative distribution function as estimated by orthogonal transformed indicator kriging. Five decomposition algorithms are considered: spectral, Cholesky, symmetric, Cholesky-spectral, and simultaneous decompositions. Impact of the algorithms on spatial orthogonality and order relations problems is examined and their performances together with indicator kriging are compared using a real dataset. |
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Keywords: | spatial orthogonality factor variograms order relations problems |
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