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Results suggest that both models perform comparably well in terms of correlation coefficient and root mean square error values.
Numerical comparisons suggest that, when combined with appropriate variable selection strategies, rules derived from one-factor models perform comparably, or better, than the most successful extant alternatives under the conditions they were designed for.
For the Poisson example, we see that all of the considered models perform comparably well.
The negative binomial model slightly outperforms the CMP model, although both models perform comparably well, based on their respective AIC values (Burnham and Anderson 2002).
We found that Jenkins and Chojnacky models perform comparably, but that at both a field plot level and a total county level there was a ~ 20% difference between these estimates and the CRM estimates.
On journal data, all three OKLC models perform comparably well.
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All three models performed comparably with slightly better performance by FIA's CRM method and slightly poorer performance using the Chojnacky estimates.
As in the balanced design, the fixed- and random-effects models performed comparably for various ICC values, largely because the fixed and random intercepts for study centres cancelled out in estimating effect contrast when we fit Models B and C, and had little impact on the estimation of the fixed effect contrast across centres.
A significant boost of their retrieval performance is reported not only compared to their original global form, but moreover, the proposed local features tested in the most straightforward retrieval model perform comparably and even outperform some of the most recently proposed retrieval models that base their success in much more complex data manipulations.
Fixed-effects model performed comparably to the mixed-effects model under most circumstances but lost efficiency when many centres contained a relatively small number of patients.
We have evaluated our generalised binding model and found it to perform comparably to classical restricted motifs as found by MEME.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com