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We additionally performed best-case and worst-case scenario simulations for each population in question.
Moreover, we will perform best case/worst case analysis considering incremental values for cost and effectiveness (lower costs and better effectiveness vs higher costs and lower effectiveness).
To strengthen our findings, we used a propensity score to perform a best case-matched analysis.
The structure of this section is to discuss different cases where the system performs best, and cases where the system does not perform favorably.
The dictionaries performed best with case-sensitive matching but the differences with partial case-sensitive and with case-insensitive matching were marginal.
Both the pretreatment and the EH performed better than the best case observed in our study.
It can be seen that Gabor features perform best in most of the cases and are therefore better suited for describing chimpanzee faces than simple pixel-based features.
For sensitivity testing, item-by-item analyses were then performed assuming worst case and best case scenarios.
In the best case, these matrices perform nearly as well as dense Gaussian random matrices, despite having many fewer nonzero entries.
When data is omitted at random, the local methods perform best; namely the Spline in a case of a very low corruption rate and linear interpolation as the rate of corruption increases.
In sum, only in two out of nine non-trivial cases, models that perform best with respect to RMSE lead also to the best RMSEm performance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com