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The validity of this model was assessed by the parametric bootstrap approach using the sum of the squares of the deviations between observed and expected mismatch distributions as a statistic as implemented in ARLEQUIN.
To account for non-normality of the cost data, we estimated 95% confidence intervals (95% CIs) for differences between groups, applying a non-parametric bootstrap approach using the percentile method [ 37, 38].
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To account for non-normality of cost data, 95% confidence intervals (CI) were estimated via a non-parametric bootstrap approach using a percentile method based on 1,000 replications.
In order to account for non-normality of the cost data, confidence intervals (CIs) were estimated for each BMI group applying a non-parametric bootstrap approach using a percentile method [ 38].
A third limit of the parametric bootstrap approach used to investigate power and accuracy is that it relies on parameters estimated from the data.
As a final measure of gene detection in this data set, we utilized a bootstrapped data resampling approach using the distribution of reads in our final assembly (see methods section) to generate a unigene accumulation curve which plots the number of unigenes detected as a function of sequencing effort.
We employed a parametric bootstrapping approach, using coalescent simulations of population structure to determine which evolutionary histories would be most likely to produce the observed pattern of genetic diversity.
Felsenstein, J. Confidence limits on phylogenies: an approach using the bootstrap.
Felsenstein, J. Confidence Limits on Phylogenies: An Approach Using the Bootstrap.
Felsenstein, J. Confidence-limits on phylogenies - an approach using the bootstrap.
Nonetheless, the highly stringent bootstrapping approach used herein ensured robustness of the model.
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