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A bootstrap method was implemented by repeatedly sampling input data distributions to calculate a population of optimal parameter fits.
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In order to evaluate whether a particular propensity value was significantly different from 1 (either above or below), a statistical bootstrapping method was implemented similar to [10].
Bootstrapping method was implemented in order to obtain bias-corrected 95% confidence intervals (95% CIs).
Both of the neighbor-joining (NJ) and maximum likelihood (ML; JTT model, bootstrap 100) methods were implemented in the software MEGA5 [ 42].
To determine the accuracy of our model, Bootstrap method was used, which can be implemented by obtaining a number of re-samples of our observed dataset [ 13].
In this paper, the bootstrap method was used to estimate the frequency of a signal.
Also, a bootstrap method is proposed to obtain critical values.
The bootstrap test was implemented using 500 replicates.
A total of 100 bootstrapping replicates was implemented with a ML approach.
Bootstrapping of 100 datasets was implemented.
28 Validation was implemented using bootstrapping.
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