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As a result, it recommended using the bootstrap method for the combined LRLs.
The present example demonstrates that using a bootstrap method for the estimation of outcome-probability confidence intervals provides useful additional information about score-class statistics, guiding physicians towards the most convenient model for predicting morbidity outcomes in their clinical context.
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In addition, the paper introduces an alternative Markov model for analyzing the up-and-down method which improves that proposed by Chao and Fuh [Chao MT, Fuh CD. Bootstrap methods for the up and down test on pyrotechnics sensitivity analysis.
Since we use the bootstrap method for computing the standard errors, the complex matching does not present any analytical problem.
To justify the choice of the bootstrap method for selecting the optimal smoothing parameter, we have presented the integrated squared error as a function of the smoothing parameter h. Figure 2 shows the obtained results with bootstrap, cross-validation, and rule-of-thumb methods.
In this paper, we apply the bootstrap method for calculating the lower confidence bounds of the index CTPU, and determine the sample size for a specified estimation accuracy.
The results in Appendix Appendix 3: bootstrapping about using the bootstrap method for calculating the 95% confidence interval of the fading parameter are satisfactory.
Furthermore, we have applied the bootstrap method for selecting the optimal smoothing parameter which makes the proposed semi-analytical method more accurate.
The bootstrap method for investigating the model building is not new, but is still somewhat experimental.
Furthermore, we applied a bootstrap method for selecting the optimal smoothing parameter to make the proposed semi-analytical method more accurate.
Furthermore, in order to control the behavior of the probability density estimator, we applied a bootstrap method for selecting the optimum smoothing parameter.
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