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When there are few admixed individuals, both methods perform well (relative to their average performance) even with as few as 10 unmixed individuals in the dataset.
As expected, all methods perform well when the SNR is high, but exhibit marked differences in performance for finite SNR.
In 'Flixster', all the methods perform well.
Though these methods perform well in practice, they use a linear structure.
We then show that our methods perform well for sparse data sets.
Giraitis et al. (2013) find that such methods perform well in forecasting several US macroeconomic series.
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All methods performed well with no errors.
Both methods performed well on our data.
Regression-type methods performed well in this type of challenge.
We found some disagreement between which methods performed well when different negative data sets were used.
When tested against promoters, none of the methods performed well, suggesting the promoters were not suitable as negative controls.
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