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All other models had lower dependencies (VIF less than 1.6).
When there was impact between groups, complex models had lower bias under certain conditions, but were not as efficient as simple models in estimating DIF parameters.
In general, the simplest models had lower performance statistics.
The Spitz and Bach models had lower sensitivity but better specificity than did the LLP model.
The prospective prediction models had lower predictive R-squares than the concurrent prediction models.
Since the S models had lower AICc values, tables and graphs in the following section show the results of S models complemented by additional results from P models.
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Moreover, SDCC models have lower MSE than DCC models.
The better history-matched models have lower objective function values.
When compared with integrated ANNs, the lumped ANN and LLR models have lower precision to simulate monthly streamflow in arid inland basin.
For small number of antenna element pairs, correlation-based models have lower computational complexity while the geometry-based stochastic models (GBSMs) can provide more accurate modeling of real radio propagation.
To assess the relative fit of each model, we compared the Akaike's Information Criteria (AIC), with better fitting models having lower AIC [ 31].
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