Sentence examples for bootstrap approach for the from inspiring English sources

Next, we evaluate the performance of our bootstrap approach for the inference of a network with a hierarchical structure.

We recommend the new Wild Bootstrap approach for the selection of biomarkers in early diagnostic trials, especially for high accuracies and small samples sizes.

We calculated the mean, median, 25th, 75th and 90th centiles (and respective 95% confidence intervals (CIs), estimated using a bootstrap approach) for the patient, primary care and overall pre-referral intervals, by cancer site.

In order to measure the performance of our bootstrap approach for the inference of the hierarchical organization of the network components from the simulated data we developed two measures.

Faraway [ 15] and Samuh, et al. [ 16] applied the parametric bootstrap approach for testing the variance component.

For the usual bootstrap approach, for each bootstrap cycle, 108 cities would be chosen with replacement from the original set of 108 cities, and the maximum likelihood estimate (MLE) of the pollutant effect computed using Poisson regression methods.

Results from such an analysis can be found in Fig. 11, using Cox proportional hazards models to estimate the causal effect in Eq. 4, and the latter bootstrap approach for confidence intervals.

In this study a bootstrap approach for estimating confidence intervals of outcome probabilities is described and applied to design and optimize the performance of a scoring system for morbidity in intensive care units after heart surgery.

The aim of this study was to investigate and discuss the accuracy and precision of estimators of variance components for upper arm elevation when data are collected using different sampling strategies, and to suggest and apply a bootstrap approach for investigating sampling performance in this context.

Using a bootstrapping approach for data analysis, the results of the economic analysis will be presented using cost-effectiveness acceptability curves.

Here, we present a parametric bootstrapping approach for time-course data, in which Gaussian process regression (GPR) is used to fit a probabilistic model from which replicates may then be drawn.