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Given n individual source waveforms, bootstrap samples were constructed by random selection of n waveforms with replacement.
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The wild bootstrap samples are constructed by adding the bootstrap residuals : (16).
(5) The wild bootstrap samples are constructed by adding the bootstrap residuals : (16) We use to denote a wild bootstrap dataset.
Subject-level bootstrapping ensures that bootstrap samples are constructed from independent units, or in this case, subjects, with correlated replicates collected from those subjects.
Each bootstrap sample is constructed by randomly sampling, with replacement, the same number of instances as the original data.
Ten thousand bootstrap samples were then obtained to construct 95% confidence intervals for sensitivity and specificity at a range of potential cut-off values.
The method to create the balanced bootstrap samples is to construct a number of instances X 1, X 2, X 3,…, X n repeating B times so that a sequence of Y 1, Y 2, Y 3,…,Y Bn is achieved.
One hundred bootstrap samples were generated using the SEQBOOT program [51].
First 100 bootstrap samples were generated.
Due to small sample size, 95%% bootstrap confidence intervals based on 10,000 bootstrap samples were obtained.
First, 500 bootstrap samples were generated from the original sample.
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