using importance sampling or Monte Carlo simulation methods. Our method is different in the way that it allows us to relax … simulation or sampling techniques. Using Kalman filter and smoother equations, we derive a set of moment conditions, and … and importance sampling estimation methods. …

Systematic sampling is easy, efficient, and widely used, though it is not generally recognized that a systematic sample … of them determine which estimators are unbiased, when using the sampling design as the basis for inference. We describe … selection. When the population size is known at the outset, systematic sampling with unrestricted selection is most likely the …

inventory of fine woody debris (FWD); however, the sampling protocols involve tallying only the number of FWD pieces by size … class that intersect a sampling transect with no measure of actual size. The line intersect estimator used with those …

Binomial sampling based on the proportion of samples infested was investigated for estimating mean densities of citrus … parameters adequately described a validation data set from a sampling plan consisting of 192 samples. Projections indicated the … fitted parameters would apply to sampling plans with as few as 48 samples, but reducing sample size resulted in an increase …

. Projections indicated that a sampling plan consisting of 10 trees and eight flush shoots per tree would provide density estimates … presence-absence sampling plan with a fixed precision level was developed and can be used to provide a quick estimation of D …

both simple random and cluster sampling. The BRR estimate of variance is the sum of variances and covariances of unit …-level kNN estimates in the area of interest. In Monte Carlo simulations of simple random and cluster sampling from seven … and Monte Carlo sampling variances was good both for population and for small-area totals. The modified BRR estimator is …