Sentence examples for allowing a more robust from inspiring English sources

Senator Jesse Helms and other leading conservative Republicans believe that the ABM treaty is an antiquated relic of the cold war and should be scrapped, thus allowing a more robust missile defense than the one the administration is proposing.

While avoiding the potential aggregation bias of subgroup analysis and allowing a more robust comparison of intervention characteristics, these trials are unfortunately limited by short follow-up periods and generally report only on risk factors or biological parameters, making it difficult to assess their impact on the long-term clinical outcomes observed in our analysis.

In addition, we have included data from additional assays to have even more data points allowing a more robust statistical analysis.

We used fixed effects analyses that remove all observed and unobserved time-invariant confounding, allowing a more robust assessment of causal associations than is possible with non-repeated-measures data.

The computer-assisted assessments enabled us to evaluate QOL at many time points with short intervals, allowing a more robust estimate of longitudinal QOL changes during each treatment course.

Our population was small due to the scarcity of this disease, and a larger population would have allowed a more robust multivariate analysis.

This would allow a more robust information on thresholds to conserve in the best possible manner "old-growthness" (sensu Bauhus et al. (2009)) in managed forest ecosystems.

In particular, sudden removal may allow a more robust assessment of an intervention's long-term impact by minimising 'learning effects'.

Technically, the implementation of high-throughput sequencing analysis and genomic technologies are rapidly becoming part of standard practice, which may ultimately allow a more robust prediction of response to immune checkpoint blockade.

In the future, more extensive data, both from unthinned and thinned stands covering wider range of ages, densities and site qualities, would allow a more robust model to be developed, that could be extended to model the effects of climate and nutrition, and biomass and carbon outputs.

To accord with this result, we conclude that the utilization of the FFA as an add-in optimizer in a hybrid data-intelligent model leads to a significant improvement in the predictive accuracy, presumably due to the optimal weights attained in the hidden layer that allows a more robust feature extraction process.