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Bootstrapping is a method for assigning measures of accuracy to sample estimates and allows estimation of the sampling distribution [ 40].
The model combine a suitable prior distribution with sample estimates of probability-weighted-moments, which exhibit much smaller sampling uncertainty than estimates of ordinary moments.
In forest inventories, various sampling techniques, also known as sampling designs, are used for the collection of the necessary information, which will provide precise sample estimates of the tree population characteristics at a low cost.
Then, we defined 16 scenarios to calculate the sample composition using an algorithm that incorporated data from a pilot sample, estimates of cost, and prior specifications of the expected error and confidence level.
At the same time, the estimated means of these three imputed variables over the entire study area were very similar to the representative sample estimates using the ground data only.
These larger sample estimates are reported in Table 11 in the "Appendix B".
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Piccolo, S. R., Withers, M. R., Francis, O. E., Bild, A. H. & Johnson, W. E. Multiplatform single-sample estimates of transcriptional activation.
So in this paper I describe a multiple imputation (MI) method for calculating design effects associated with pooled-sample estimates.
I also evaluate the method presented, by simulating NHANES individual sample data from which artificial pools are created for use in a comparison of pooled-sample estimates with estimates based on individual samples.
In their place, the measures of uncertainty output by SEATS for its finite-sample estimates are measures for semi-infinite data estimates, used as approximations.
Its series are shown to have canonical model-based decompositions whose finite-sample estimates, filters, and error covariances have simple revealing formulas from basic linear regression.
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