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The purpose of this paper is to use Monte Carlo and bootstrap methods to estimate nonlinear parameter uncertainties with a Microsoft Excel spreadsheet.
We used a bootstrap methods to estimate 95% confidence intervals for the SRM and ES [ 23].
Alternatively, under the location shift assumption, we can use bootstrap methods to estimate pNoether.
We will use bootstrap methods to estimate standard errors and confidence intervals for these cost-effectiveness measures.
Since the standard error of the SRM is not defined we used bootstrap methods to estimate 95% confidence intervals for the SRM.
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As an alternative, Simar and Wilson (2007) suggested using a bootstrap method to estimate bias-corrected efficiency scores during the first step.
We calculated standard errors and Z-scores using the bootstrap method to estimate significance of the D values.
For continuous scale tests, weighted linear regression is used to estimate the parameters for each curve, including a bootstrap method to estimate the standard errors.
We converted permutation-derived P-values to q-values with the QVALUE software, using the bootstrap method to estimate π0 and the default λ tuning parameters (Storey et al. 2004).
For our analysis of individual fitness as determined from our age-specific project matrices, we followed the advice of Caswell (2001) and used a bootstrap method to estimate 95% confidence intervals (Kalisz and McPeek 1992) as implemented in Matlab (The Mathworks).
We applied cross validation and bootstrapping methods to estimate the accuracy of models developed in earlier stages.
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