Estimation of spatial covariance structures by adjoint state maximum likelihood cross validation: 2. Synthetic experiments

F. Javier Samper, Shlomo P. Neuman

Research output: Contribution to journalArticle

25 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)363-371
Number of pages9
JournalWater Resources Research
Volume25
Issue number3
DOIs
StatePublished - Mar 1989

ASJC Scopus subject areas

  • Water Science and Technology

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