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Howard et al. (2014) assessed 14 parametric and nonparametric methods using simulated genetic architectures, and found that parametric methods performed slightly better than nonparametric methods for additive genetic architectures, but parametric methods had difficulty in capturing non-additive effects such as epistatic effects.
With low substitution rates, the methods performed slightly worse with an increasing number of unique sequences (or haplotypes).
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Model performance statistics indicated that the curve number method performed slightly better than the Green and Ampt method.
Two spatial interpolation methods were used for generating the gridded rainfall dataset and the universal kriging method performed slightly better than the inverse distance weighting method.
While our SR method performed slightly better than the bicubic interpolation (0.0148, 0.0382, and 0.0364 improvement), the results of images in Fig. 18 show that the edges are not observably better than the bicubic interpolation method.
For data sets with 10 loci, the K = rv method performed slightly better.
The LASSO method performed slightly worse than the random forests method, but better than the SVM method.
By carefully weighting the relative importance of different data sets and using elastic net for soft integration, their method performed slightly better than our simple KNN model.
KNN method performed slightly better than SVM.
In general, the MGD method performed slightly better, although there were no significant differences between the approaches across scenarios.
The averaged prediction accuracies of the three feature filtering methods were similar, but the Kruscal Wallis method performed slightly better than the other two methods.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com