Optimized spatial sampling of soil for estimation of the variogram by maximum likelihood

By R. M. Lark. 2002

164 citations recorded by the source corpus.

Subfields: Environmental Engineering, Management, Monitoring, Policy and Law, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/7cf9da7d35cfab8499c1bae533441968ce8edb84

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In Environmental Engineering

In Management, Monitoring, Policy and Law

In Management Science and Operations Research

Citations in the corpus

Spatial sampling design for prediction with estimated parameters (2006, methodology): “Simulated annealing (SA) is an optimization method that has been successfully applied to similar problems by van Groenigen and Stein (1998), Lark (2002) , and Wiens (2004).”
Optimization of sample patterns for universal kriging of environmental variables (2007, background): “Lark (2002) and Zhu and Stein (2005) present, among others, sampling methods for estimating the parameters of covariance functions (variograms), but these methods are not optimal for identifying the trend.”
Spatial sampling design for parameter estimation of the covariance function (2005, methodology): “See, for example, Kirkpatrick et al. (1983). van Groenigen and Stein (1998) and Lark (2002) apply simulated annealing in the spatial sampling design context.”
Optimized Sample Schemes for Geostatistical Surveys (2007, background): “ Lark (2002) observed that the optimized schemes were very sensitive to changes in the assumed variogram parameters.”
Spatial sampling design under the infill asymptotic framework (2006, methodology): “Royle (2002) studied exchange algorithms for spatial designs, Ko et al. (1995) employed a branch-and-bound method for maximum entropy sampling problem, and Kirkpatrick et al. (1983), van Groenigen and Stein (1998), and Lark (2002) , among others, used simulated annealing algorithm (SAA) in the spat”
Bayesian Geostatistical Design (2006, unknown): “Each of these formalises the widely accepted view that in order to compromise between prediction accuracy and efficient parameter estimation, good geostatistical designs should include close pairs or groups of sampling locations within an otherwise spatially regular arrangement (Martin, 2001; Müller”
Approximating likelihoods for large spatial data sets (2004, background): “Then r = 1, which gives three equally spaced observations, may not be a good choice since evenly spaced observation networks tend to work poorly for estimating covariance structures (Stein (1999), section 6.6, Pettitt and McBratney (1993), Lark (2002) and Zhu (2002)).”
Inhibitory geostatistical designs for spatial prediction taking account of uncertain covariance structure (2016, methodology): “Lark (2002) used likelihood estimation procedures under an assumed Gaussian process model. Pettitt and McBratney (1993) studied several sampling designs for estimating parameters using the restricted maximum likelihood method of parameter estimation. A general consensus from this body of work is tha”