In Scenario 3 (Supplementary Fig. S1), as the number of studies increases from two to four, the power tends to increase for all the four methods compared with Scenario 1, and the Bayesian method still has higher power than the other three in nearly all the cases.
Similarly, when we test our method with a weak scenario, 2-fold, our method has low power (our unpublished data).
Five projects reported a combination of the scenario method with a Delphi technique to reach consensus among participating experts [ 18, 64, 67– 71].
Overall, the multiyear estimator and smoothed-annual estimator yielded similar results, with slightly better fit with the multiyear method for scenario 3. Third, we compared model-based error with simulation error.
In this paper we introduce landscape futures analysis as a method which combines linear programming optimisation with scenario analysis in quantifying the environmental, economic, and social impacts associated with achieving environmental targets, on a landscape scale.
For imputation of CA, the results were similar between the three methods for imputation, with Scenarios 1 and 2 producing the best concordance and Scenarios 1 and 3 producing the highest mean and minimum quality score.
The proposed algorithm gives more reliable and accurate PDF results of bus voltages and branch flows comparing with GC-PLF calculation method in scenarios with different WT parameters.
There were also some estimation problems with this method, particularly in scenario 14 with 7.6% of simulations unsuccessful when estimating either e ψ or its upper or lower confidence limits.
In the benefit-harm trade-off method, hypothetical scenarios with the benefits, costs, risks and inconveniences associated with the intervention will be presented to each participant.
The t-tests were derived from a mixed model with fixed effects for method and scenario and their interaction and a random effect for simulation replicates nested within scenarios [ 6].
