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Using this method, multiple bootstrap samples are drawn from the original data set.
In this approach, bootstrap samples are drawn from the primary observer counts and for each bootstrap sample, the population size is estimated by dividing the sum of the count by the proportion of the area surveyed.
Another name for such a procedure used in the literature is m out of n bootstrap, whereas m < n and the bootstrap samples are drawn without replacement [ 31].
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Bootstrap samples were drawn from observed wild-type sample sizes ranging from 81 to 217 with a mean of 179.
Bootstrap samples were drawn with replacement and with the same size as the original sample.
Bootstrap samples were drawn with replacement (100 replications) from the full data set.
For each comparison B = 200 bootstrap samples were drawn from the training-set.
A total of 100 random bootstrap samples were drawn with replacement from the [total] group of 1,337 patients.
Based on the recommendations, 2,000 bootstrap samples were drawn to obtain overall model fit and 250 bootstrap samples to obtain parameter estimates and associated standard errors [ 17].
For subsequent checking of stability/reliability, 100 bootstrap samples were drawn from each imputed data set, resulting in 1000 'sample' data sets.
To estimate the robustness of final model results, 1000 bootstrap samples were drawn with replacement from the original audited sample and analyzed.
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