Abstract
Quadratic estimators of components of a nested spatial covariance function are presented. Estimators are unbiased and possess a minimum norm property. Inversion of a covariance matrix is required but, by assuming that spatial correlation is absent, a priori, matrix inversion can be avoided. The loss of efficiency that results from this assumption is discussed. Methods can be generalized to include estimation of components of a generalized polynomial covariance assuming the underlying process to be an intrinsic random function. Particular attention is given to the special case where just two components of spatial covariance exist, one of which represents a nugget effect.
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Marshall, R.J., Mardia, K.V. Minimum norm quadratic estimation of components of spatial covariance. Mathematical Geology 17, 517–525 (1985). https://doi.org/10.1007/BF01032106
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DOI: https://doi.org/10.1007/BF01032106