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Estimation of spatial covariance structures by adjoint state maximum likelihood cross validation: 2. Synthetic experiments
Author(s) -
Samper F. Javier,
Neuman Shlomo P.
Publication year - 1989
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr025i003p00363
Subject(s) - identifiability , covariance , variogram , mathematics , stability (learning theory) , cross validation , kriging , autocorrelation , spatial analysis , statistics , estimation theory , algorithm , computer science , machine learning
Paper 2 of this three‐part series uses synthetic data to investigate the properties of the adjoint state maximum likelihood cross‐validation (ASMLCV) method presented in paper 1 (Samper and Neuman, this issue (a)). More than 40 synthetic experiments are performed to compare various conjugate gradient algorithms; investigate the manner in which computer time varies with ASMLCV parameters; study the effect of sample size and choice of kriging points on ASMLCV estimates ; evaluate the ability of various model structure identification criteria to help select the most appropriate semivariogram model among given alternatives; study the conditions required for parameter identifiability, uniqueness, and stability; quantify the statistics of cross‐validation errors; test hypotheses concerning the distribution and autocorrelation of these errors; and illustrate the computation of approximate quality indicators for ASMLCV parameter estimates.