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For instance, for the results reported in "Illustration: implementing the proposed schema on the 'DITCH' agent-based model" section, we performed multiple sensitivity analysis techniques such variable importance method, recursive elimination method, and PRCC (partial rank correlation coefficient).
To overcome this limitation, we varied these assumptions extensively in multiple sensitivity analysis.
Conducting multiple sensitivity analysis on all outcomes is often neither practical, nor necessary.
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Automatic differentiation (AD) is used to perform a multiple parameter sensitivity analysis for the Los Alamos sea-ice model CICE.
It is always possible to go further, and we are intrigued by the suggestion of Burstyn et al. to conduct an extended, multiple-bias sensitivity analysis.
For simplicity, we have reported adjusted results after multiple imputation analysis throughout in the text but included in tabular format a comparison of the results from complete case, multiple imputation, and multiple imputation with sensitivity analysis methods.
Missing values were handled by using multiple imputations, and sensitivity analysis showed no significant difference in the variables (P > 0.05).
As a result of using multiple methods of sensitivity analysis, the stability of the results and its limitations were demonstrated.
We addressed this problem with different analytical strategies including complete cases only and multiple imputations including sensitivity analysis.
The effects of operational parameters are further evaluated by the multiple regressions and the sensitivity analysis.
For this reason it becomes practically impossible to perform a robust and comprehensive analysis that requires multiple simulations such as sensitivity analysis, uncertainty quantification and optimization.
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CEO of Professional Science Editing for Scientists @ prosciediting.com