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When TEP takes into account a variety of uncertainties such as load and wind power output, a commonly used approach is using Monte Carlo (MC) simulations to sample uncertainty scenarios.
As each HDSS covers a total population, rather than a sample, uncertainty intervals are not shown.
Additionally, bootstrapping was used to explore sample uncertainty (5,000 replications).
Though we accounted for sample uncertainty using bootstrapping, dropout may have been an issue.
The bootstrap analysis, which corrected for the sample uncertainty, revealed that the study results are quite robust.
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.
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In addition, bootstrap simulations exploring sample uncertainties were also favorable for EXP.
Sampling uncertainty is represented by probability distributions for AFP.
They were mostly due to the sampling uncertainty.
The sampling uncertainty surrounding the other parameters, say (varvec{vartheta }), is not necessarily so.
An ensemble of outcomes from stochastic downscaling allows for addressing the sampling uncertainty.
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