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Bootstrap simulation is used to generate the risk profile savings in vehicle operating cost, and in travel time resulting from demand-responsive traffic.
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Bootstrap simulation was used to estimate the standard error for each parameter.
A Jack Knife approach was used to calculate rates and confidence intervals for the binomial count data and bootstrap simulation was used to determine 95% confidence intervals for the detection rate ratios and the number needed to treat.
1000 bootstrap simulations were used to test the reliability of branching.
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.
To determine the corrected P-values on the basis of the maximal χ analysis, 2000 bootstrap-like simulations were used in univariate analyses to estimate the distribution of the maximal χ statistics under the null hypothesis of no association.
More general methods such as Monte Carlo and bootstrap simulation can be used to estimate the parameter uncertainties.
All analyses are planned to be carried out with SPSS 19.0 and MLWin 1.10, while bootstrap simulations are done using Excel.
Bootstrap simulations will be used to estimate cost effectiveness acceptability, net benefit statistics and the likelihood that group psycho-education is cost effective compared to group support.
For achieving robust estimated results, a bootstrap technique is used.
In the next section, a simulation study is used to compare bootstrapping for the AMA to existing parametric methods for quantifying spread.
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