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To analyse the influence of sampling effort and sample size on water classification, a bootstrap approach was applied to 55 bacteriological quality datasets of several beaches in the Balearic Islands Spainn).
A non-parametric bootstrap approach was applied to the population of residual values (Ndata = 63 or 105) to estimate confidence intervals and significance of the linear regression parameters [36].
To study the effects of limited sites, a bootstrap approach was applied.
A parametric bootstrap approach was applied to test the goodness-of-fit of the described models in the analysis of CON and FAT scores.
To reduce bias and increase efficiency as much as possible, the EMB algorithm combining the classic EM algorithm and a bootstrap approach was applied to impute data.
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This approach was applied here.
A TSU approach was applied.
Field data in which no birds are detected by either observer when acting as the secondary observer, but many birds are detected by primary observers, would indicate a high detection probability where the proposed bootstrap approach could be applied.
A bootstrapping approach (one thousand bootstrap samples) was applied to represent the uncertainty around the mean outcome and cost estimates.
Bootstrap analysis (1000 bootstraps) was applied to provide confidence levels for the tree topology.
Our bootstrap approach is robust to overfitting errors, and therefore does not require a cross-validation approach.
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