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Due to the uncertainty on the parametric assumption of the distributional forms, confidence intervals were calculated using a bootstrapping simulation process which is a data-based simulation method for assigning measures of accuracy to statistical estimates, used to produce inferences such as confidence intervals without knowing the type of distribution from which a sample has been taken.
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We used a bootstrap simulation to estimate the statistical significance of the co-localization between retroviral integration sites and miRNAs (see Materials and Methods).
The model was then internally validated using a bootstrapping technique.
Velocities were calculated by using a bootstrapping procedure (mean±s.d.; N=1,000 bootstrap estimates from re-sampling with replacement).
Second, a computational algorithm was developed in order to obtain an estimation of the growth parameters that one would obtain using a lower number of isolates and/or replicates, using a bootstrap procedure with 5000 simulations.
We tested this difference using a bootstrap approach and in all Monte Carlo simulations no simulated frequency value higher than the observed one was observed for chromosome X.
Using a bootstrap procedure [ 59] based on 1000 simulations per path, we assessed whether each path could be explained by specific movement rules defining CRW: move length follows a lognormal distribution and turning angle follows a von Mises distribution, both with parameters estimated in homogeneous groups of paths defined by the analysis of these variables.
Section V describes a simulation experiment to investigate power and Type I error, using a bootstrap-based method for testing omnibus significance.
N = total sample size of the trials In each simulation run t, criterion weights wkt are obtained by using a bootstrap resampling method.
The distribution of P values across the simulations can be tested against the uniform distribution using a bootstrapped Kolmogorov-Smirnov test [ 40, 41].
Field-significance was tested using a bootstrap approach.
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