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The results of bootstrapping analyses also confirmed that the generated models are robust and reliable.
These mental models are robust to demographic factors like gender and web expertise.
Our computational experiments indicate that the proposed models are robust and that the proposed algorithm is computationally simple.
The statistics exhibited in Table 2 and the graphs presented in Figs. 3 and 4 show that the estimated models are robust.
Taking into account only the selected models, we can observe in Fig. 11 that all models are robust in (h = 1).
We have examined if grain size distributions predicted by shattering models are robust against the change of various parameters and formulations.
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Knowing that models were robust, we proceeded to investigate their relative regression coefficients.
Our results appear to be robust to self-selection and heterogeneity issues although the models were robust only in OLS while the FE models were not robust.
Cross-validation ROC curve analysis showed that both the 3-month and 12-month models were robust.
Our selected models were robust to this increased flexibility.
Both models were robust to changes in most parameters.
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