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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).
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Fifth, we undertook bootstrap simulation to provide estimates of measuring uncertainty for any decision to stop early, in order to support the analyses based on a single CP estimate from each trial.
These validation experiments are not permutation simulations to estimate the empirical significance level of parameter estimates, and are not a standard bootstrapping technique because they use sampling without replacement.
Then, we performed ordered logit models with bootstrap simulation to identify the determinants of reservation wage.
Extensions of the bootstrap simulations to more complicated test-statistics are discussed.
Bootstrap simulation was used to estimate the standard error for each parameter.
Bootstrap simulations are used to estimate sample uncertainty around the cost-effectiveness ratios and are plotted in cost effectiveness planes, in which the position of the bootstrapped cost-effectiveness pairs gives an indication for possible superiority of one treatment over another.
Furthermore, it extends Kriging to random simulation, and discusses bootstrapping to estimate the variance of the Kriging predictor.
The bootstrap procedure is not the typical bootstrap to estimate uncertainty on parameters.
We also employ a parametric bootstrap approach to estimate the mean squared prediction error (MSPE) of the PEB predictor.
We use 10k bootstrap samples to estimate the confidence interval of the correlation coefficient.
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