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Moreover, the bootstrap results can be used to analyse the casualty risk estimator from a theoretical point of view.
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Hierarchical clustering results can be visualized as interactive heatmaps and plain trees, and the reliability can be checked using bootstrapping.
In future research, a bootstrap DEA can be employed for more exact results.
Our results clearly show that the Monte Carlo and bootstrap methods can be used to estimate the parameter uncertainties using spreadsheet methods.
Alternatively, a bootstrap approach can be followed.
In the latter case, bootstrap support can be misleading.
Bootstrap solutions can be found in 11 and 12.
In principle, the bootstrap strategy can be expanded accordingly.
We developed a nonparametric bootstrap similar to standard bootstraps used for phylogenetic reconstructions, and also a parametric bootstrap that can be scaled to very large genomes.
The bootstrap CoD estimator can be very slow to compute due to the complexity of.
In the context of missing data, the Bayesian bootstrap can be used for multiple imputation.
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