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To avoid reliance on asymptotic theories for inference, we utilized a bootstrap method for computing standard errors.
The assumption of multivaraite normality was not met in this data sample, we therefore utilized a bootstrap method to obtain unbiased estimates and standard errors.
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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.
After calculating path estimates in the structural model, PLS utilized a bootstrapping technique to acquire the corresponding t-values.
Also, a bootstrap method is proposed to obtain critical values.
The precision of the model parameter was determined by a bootstrap method.
A bootstrap method is used to evaluate the stability of the solution.
A bootstrap method is also discussed, and its performance is evaluated through simulations.
A bootstrap method was incorporated to estimate uncertainty and generate more robust predictions of performance.
Finally, we describe a bootstrap method for choosing values of the design parameters.
c Uncertainties in f c estimates evaluated by a bootstrap method.
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