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While RIN values are typically used to predict sample performance, we only observed a weak correlation between RIN values and ROC AUC (r2 = 0.27, Figure 2), and note that even samples with RIN values as low as 2.7 displayed robust hybridization.
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To further investigate the models' out-of-sample performance, we apply a rolling window forecast similar to the method used in [10, 49].
To evaluate and compare the finite sample performance of these interval estimators, we employ Monte Carlo simulation.
We investigate finite sample performance of each of the estimators by varying the sample size within each unique simulation experiment.
In the first simulation, we evaluate the finite sample performance of α ˆ.
In this section, we investigate the finite sample performance of the adaptive group bridge method through simulations and a real data application.
Through an extensive Monte Carlo simulation study, we compare the finite sample performance characteristics of the estimators discussed in this paper.
Simulation studies are performed to obtain the small sample performance of the proposed statistic.
We compare these approaches on their finite sample performance by Monte Carlo.
We conduct a simulation study to compare the finite sample performance of our preferred M-estimator with that of three other estimators.
In this subsection, we carry out some Monte Carlo experiments to show the finite sample performance of the proposed method.
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CEO of Professional Science Editing for Scientists @ prosciediting.com