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

F. J. Samper, Shlomo P Neuman

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Uses synthetic data to investigate the properties of the adjoint state maximum likelihood cross-validation (ASMLCV) method presented in paper 1. 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 auto-correlation of these errors; and illustrate the computation of approximate quality indicators for ASMLCV parameter estimates. -from Authors

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

Fingerprint

Maximum likelihood
kriging
autocorrelation
statistics
experiment
Experiments
Model structures
Autocorrelation
Identification (control systems)
testing
Statistics
sampling
parameter
methodology
distribution
indicator
effect
method
test

ASJC Scopus subject areas

  • Aquatic Science
  • Environmental Science(all)
  • Environmental Chemistry
  • Water Science and Technology

Cite this

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