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Analysis of the model robustness revealed collinearity problems between the two small area characteristics, income and education.
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The effect of lesion modeling in network robustness reveals the importance of this objective as the influence of the lesion is likely to depend on the global embedding of the affected vertex.
Model robustness and parameter estimation.
Measurement data, dynamically incorporated into a system executing model, improves model accuracy, accelerates simulation analysis, and/or enhances model robustness.
The tradeoff between solution robustness and model robustness is also analyzed in the obtained results.
Next, sensitivity analysis has been presented for solution robustness and model robustness.
Sensitivity analyses evaluated model robustness.
Herein, we created a new robustness analysis method called 'consistent robustness analysis' which intends to evaluate model robustness independently of operating parameters.
Model robustness was tested using both deterministic and probabilistic sensitivity analyses; results proved the model reliability.
Model robustness was assessed by entering excluded variables back into the final models.
What this model revealed was remarkable.
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