Latin hypercube sampling with dependence

An approach which can yield precise estimates of output statistics with a lesser number of samples than simple random sampling. Stochastic Methods Numerical methods available for an Stochastic approach. At 5 and 12% sampling proportions, Latin Hypercube Sampling and stratified random sampling produced the best approximations of population parameters of soil C distribution, and simple random sampling produced better mean estimates with higher precision than systematic random sampling. Select a numerical method to use when evaluating the Sampling Fit approach. Latin hypercube sampling introduces dependence between elements of the sample. Kriged estimates incorporate spatial correlations and resulted in higher standard errors, which may reflect an underestimate of the errors associated with the plot-based samples due to autocorrelation. As in (4.57), let Z')., Z') denote a Latin hypercube sample from N(0. Spatial structures were also not consistent between blocks suggesting a covariate other than landcover. Many of the soil properties demonstrated dependencies equal to the maximum lag size, suggesting dependencies outside the scale of our plots. But it is known that the random variables based on Latin hypercube sampling in dimension d are actually -negatively dependent with ed, and the resulting. The ability of four sampling methods and five different sample sizes to estimate soil C and its distribution based on stratification of landcover and topography variables were also investigated. Latin Hypercube Sampling (LHS) Latin Hypercube Sampling (LHS) is a variant of Quasi-MC method, which has been widely used to efciently spread samples into the entire sampling space without any overlapping 17 as shown in Fig. The spatial dependencies and structures of soil C and related soil properties under pine, hardwood, and pasture landcover in the Piedmont area of Georgia are compared, as well as the C estimates and errors of plot averages vs.

Latin hypercube sampling with dependence