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Exact(12)
Bootstrap samples were drawn from observed wild-type sample sizes ranging from 81 to 217 with a mean of 179.
For each comparison B = 200 bootstrap samples were drawn from the training-set.
Bootstrap samples were drawn with replacement (100 replications) from the full data set.
Bootstrap samples were drawn with replacement and with the same size as the original sample.
A total of 100 random bootstrap samples were drawn with replacement from the [total] group of 1,337 patients.
Bootstrap samples were drawn with replacement (n = 1000) from the two data sets (N = 681 and N = 512, respectively) and were used to compute adjusted AUCs.
Similar(47)
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].
Should they be the same across the imputed data sets or should separate bootstrap samples be drawn from every imputed data set?
This means that a bootstrap sample is drawn in the first step.
More suggestions(17)
bootstrap samples were performed
bootstrap samples were stratified
bootstrap samples were developed
bootstrap models were drawn
bootstrap resamplings were drawn
bootstrap samples were produced
bootstrap samples were chosen
bootstrap samples were selected
bootstrap samples were based
bootstrap samples was drawn
bootstrap samples were sorted
bootstrap statistics were drawn
bootstrap samples were used
bootstrap samples were calculated
bootstrap replicates were drawn
bootstrap proportions were drawn
bootstrap probabilities were drawn
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