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1– 4 The purpose of this study is to use the bootstrap method to perform tests of the generalizability of national DRG tariffs with respect to local setting.
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This study uses the Koenker (2004) method to perform the panel quantile regression with a fixed effect, while the Bootstrap method is used for the estimate of standard error.
In the absence of an external validation cohort, internal validation was performed using the bootstrap method to assess the optimism of our clinical diagnostic models., In this context, optimism refers to how much better a diagnostic model performs in the population in which it was derived compared with a new population in which it is validated.
We adopted the bootstrap method to conduct regression analysis.
We used a bootstrap method to obtain confidence intervals [32].
We performed bootstrapping method to calculate a 95% confidence interval of the c-statistics of each model.
Second, to obtain an empirical null distribution, a bootstrap method was performed for each individual, in which 2,000 trial-randomized BOLD time series of each ROI were computed.
Second, to estimate the empirical threshold for Zg, a group-level bootstrap method was performed, in which 2,000 bootstrap samples of Zg collapsed across ROIs and participants were computed as the empirical null distribution of Zg, with the empirical threshold (P<0.05) then determined as Zt.
Permutation and bootstrapping methods are used to perform inference on the change points.
To further evaluate differences in discrimination between models, pair-wise AUC comparisons using bootstrap method were performed with a ROC comparison package [ 32].
The bootstrap method also performed worse for models with small tree sizes (2-4 inodesal nodes) for the MEL0501 data-set as well as for trees with 10 and 14 internal nodes for the MEL0101 data-set.
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