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Several investigators have compared the predictive performance of these methods using simulation and empirical data (VanRaden 2008; Riedelsheimer et al. 2012b; Howard et al. 2014; Wang et al. 2015).
We first evaluate the performance of the selected methods using simulation study.
We evaluate this rule in comparison to competing methods using simulation studies in the next section.
A comparison of the five methods using simulation data shows that the piccolo method is most robust against noise.
We also investigated the effect of sample size on the different methods, using simulation scenario G, which aims to estimate five parameters (R1, R2, S1, S2, and Ip).
The purpose of this study is to evaluate the performance of the two methods using simulation in several scenarios when outliers exist and to provide insight into the selection of the appropriate models.
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The proposed method is tested and compared with other methods using simulations and found to have superior performance to the Eigenstrat method when the differentiation among populations was limited.
Several studies investigating supertree methods using simulations were carried out [ 32- 35].
Fung et al. (2003) compared all these methods using simulations, and we used what was recommended in that report natural splines (ns) in time series and bidirectional case crossover.
Molecular dynamics methods using simulations in explicit solvent conditions were applied for investigating the wild and mutant amino acids and variation in APE1 protein dynamics and stability due to Asp148Glu variation.
Section 4 gives design examples to show advantages of our method using simulation results.
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