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Many other selection methods are superior.
Researchers broadly agree that the newer lines, which were derived using more refined methods, are superior to the older ones.
There has been some debate whether inverse or classical calibration methods are superior when there are multivariate predictors and some of them are missing.
These two methods are superior to conventional liquid liquid extraction since the separated acetonitrile phase can be directly injected to the RP LC system.
We compared the results of these multi-objective methods with the single-objective methods and found that the multi-objective methods are superior regarding accuracy.
The results illustrate the problem of non-uniqueness in model calibration since none of the methods are superior with respect to all performance measures considered.
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Also, the quality of digital methods is superior to analog.
The result showed that the energy efficiency of our methods is superior to other methods.
In addition, these two methods were superior to the absolute SUV and percentage SUVmax threshold methods.
Traditional teaching methods were superior to technology-assisted education in only 3/64 (5%, 95% CI 1-131-13%
The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively.
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