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The autoregressive non-linear models therefore seem to be a more robust choice for prediction of macroscopic road safety trends, as they provide desirable predictive properties, satisfy the assumptions of the model (e.g. uncorrelated residuals) and provide intuitive model parameters (in terms of magnitude and sign).
We conclude that the trend test is the most robust choice for large-scale association scans where the true G-E interaction model is unknown.
Given these studies and the fact that the models presented here do not give substantially different results, the unordered model may be a robust choice for estimating rate and time parameters from CRISPR array data.
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Thus, alkenones and Mg/Ca appear to be the most robust choices for paleothermometry in the OT.
A robust choice usually done for setting f k lies in using Equation 77 with (m,q)= 0,0), which implies that the useful coefficient is practically set to unit: Re left{ mathbf{v}_{0,0}^{ k)^{H}} mathbf{f}_{k} right} simeq 1 (79).
Any colour is better than pink, but these robust choices hardly strike one as girly.
However, we have found our results to be quite robust to this choice; for example, even much higher thresholds yield qualitatively quite similar results.
On the other hand, in the literature it is supported that GEE analysis is robust against a wrong choice for a correlation structure.
Consequently, such a scheme becomes a natural choice for designing robust fault detection algorithms for electro-hydraulic systems.
Given the obvious limitation of such a retrospective analysis, a registration study in the future may provide robust data to inform treatment choice for patients with RRPC.
Thus, our economic results remain robust to realistic choices for this threshold.
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