Sentence examples for bootstrap sampling is used from inspiring English sources

Bootstrap sampling is used to characterize the variability in the observed percentile values, thereby providing a means for assessing whether the differences seen between model predictions are significant.

Monte Carlo bootstrap sampling was used based on the incidence of ARI and computed to 95% confidence intervals (CI) from the sampling distributions.

Bootstrap sampling was used to validate the choice of variables in the models, with 1,000 samples of the same sizes as the study populations.

The multiple imputation method was used to deal with missing values, and bootstrap sampling was used to assess internal validity and refine the model.

For the interpretation of the results it is most important that all modeling steps are repeated in each bootstrap sample and that the same set of bootstrap samples is used for all strategies.

Then, the bootstrap sample is used for feature selection and classifier learning.

Bootstrap samples are used from the data to construct each tree and at each node, a random subset of predictors are tested.

In obtaining bootstrap estimates of the standard errors of each simulation estimate of the population total basal area, 100 bootstrap samples were used; more bootstrap samples might have been used, but some studies of sampling in forest circumstances have suggested that 100 is sufficient (Schreuder et al. 1992; West 2016).

In both cases, 1000 samples (of three times 50 iris) were generated and for each, 1000 bootstrap samples were used in the resampling method to determine a p-value for the test of homogeneity.

In addition, 500 bootstrap samples were used in the empirical example.

A total of 10,000 bootstrap samples was used to obtain the 95%% confidence intervals (CIs).