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PARAMETRIC ESTIMATION OF COVARIANCES OF REGIONALIZED VARIABLES 1
Author(s) -
Kitanidis Peter K.
Publication year - 1987
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1987.tb00832.x
Subject(s) - covariance , quadratic equation , mathematics , covariance function , parametric statistics , statistics , variance (accounting) , hydrogeology , component (thermodynamics) , mathematical optimization , geology , physics , geometry , geotechnical engineering , economics , accounting , thermodynamics
The usefulness of stochastic models in describing the spatial variability of hydrogeologic quantities, such as permeability, storativity, piezometric head, seepage velocity, and solute concentrations is now widely recognized. In practice, these quantities are represented as the sum of a well‐structured component, or drift, and a more erratic fluctuation component which is described statistically through its covariance function. This paper reviews some of the most recent and most promising methods for the estimation of parameters of these covariances from existing data. They are maximum likelihood, restricted maximum likelihood, minimum‐variance unbiased quadratic estimation, and minimum‐norm (weighted least squares) estimation. The applicability of such methods to conditional and unconditional probability problems is discussed.