Sentence examples for bootstrap samples to determine from inspiring English sources

We generated 200 bootstrap samples to determine the calibration and discrimination of the model.

Hazard ratios were compared using the bootstrap method (random re-sampling with replacement; 105 samples) to determine the statistical distribution of (HR1−HR2) from which the relevant P value than was derived.

A total of 1000 bootstrap tests were sampled to determine the confidence in each node on the consensus tree.

The 95% confidence intervals are calculated by a 5,000-fold 5,000-folding of a 10% sample to determine the sensitivity of our results to random network sampling variations.

We use 10k bootstrap samples to estimate the confidence interval of the correlation coefficient.

On the other hand, bagging uses bootstrap samples to build the classifiers.

Bootstrap analysis of 1,000 replicates was performed by creating series of bootstrap samples to test tree branch reliability.

For bootstrap sampling confidence measures to determine the stability of the clustering, we used pvclust which uses hclust internally.

To avoid the problem of making an arbitrary choice of a fixed time window to non-remission, we used a matched bootstrap re-sampling method to determine the time to non-remission from the non-remission group, so that potential bias and random variation were averaged out.

Therefore, we first evaluated all LUR models using a bootstrap approach to determine the sensitivity of model prediction and parameter estimates to monitor sampling.

Finally, we provide an easy to apply formula for identifying the necessary number of bootstrap samples allowing to limit the bootstrap-related error to a freely definable degree.