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The bootstrap method is based on the use of the original data to generate a surrogate population of samples, where the correlations contained in the original one are removed/destroyed.

13 The bootstrap method is based on repeated sampling from the observed data to calculate nonparametric CIs (we used 1,000 replicates).

Since the linearization method is based on the assumption of normal distribution of parameter values, the Monte Carlo and bootstrap methods may be preferred to the linearization method.

The method is based on the time frequency characteristics of the wavelet transform (WT) and the capabilities of the bootstrap distribution in statistical estimation.

Bootstrap results for each method were based on 100 iterations under similar search criteria.

From this aspect, the bootstrap method is more empirically based and the Monte Carlo method is more theoretically based.

Since these methods are based on splits, bootstrap-based weighting can be easily incorporated, which is expected to further increase the accuracy of the reconstructed trees [ 28, 32].

The variables selected at the Univariate stage were adjusted for cluster effect by Bootstrap method which is based on estimation through re-sampling with replacement from the original sample to obtain adjusted p-value and 95% Bias corrected Confidence Interval (CI).

The bootstrap method is a computer simulation technique based on random re-sampling of a dataset and subsequent analysis of the data distribution [26].

Also, a bootstrap method is proposed to obtain critical values.

The choice of method to estimate confidence intervals for PR (delta or bootstrap method) should be based on study design.