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The proposed approach is illustrated through a nanomanipulation example in simulation.
For example, in simulation studies, we used those of Birdsuite (Korn et al. 2008), which employs a Gaussian mixture model and parameter estimation by the expectation-maximization algorithm.
For example, in simulation model 3 with 300 trios, the M×O test has a type 1 error rate of 4.5% and the M×M test has a power of 73.6% at a nominal 5% significance level.
For example, in simulation studies, Heffner et al. (2010) and Bernardo and Yu (2007) found genomic breeding programs to be clearly superior over traditional recurrent selection programs for complex traits in maize.
For example, in Simulation 1 when the sample size is 400 and B = 1000 in the permutation-based LRT, running one simulation requires respectively 975 s, 702 s and 1217 s if K = 20, 30 and 40; whereas the score test only needs about 16 s, 23 s and 30 s if running ten simulations.
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This requirement may not be met in all simulations, for example in simulations with very high traffic load.
For example, in simulations by Hoo [ 16], positive effects on bias through inclusion of auxiliary variables were seen in some standard errors in a confirmatory factor analysis, though the factor loadings themselves were not affected.
However, simulation studies are not conclusive when it comes to comparing time-stratified with symmetric designs; for example, in their simulation study, Lumley and Levy (2000) reported that the time-stratified method was superior, but Figueiras et al. (2005) did not find this method to be better than the symmetric CCO.
We demonstrate and analyze the design example in both simulation and experiment.
For example, in our simulation setup with,, and, coupling of the UL and DL beamforming halves the required DL pilot overhead.
For example, in the simulation results shown in Fig. 5, the fault current takes about 126 ms to exceed the limit of breaker B41.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com