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An important drawback of these designs, however, is that no direct estimator of the design variance is available.
To solve the problem, a new simulation-based optimization method combining OptQuest, experimental design, variance analysis, and response surface methodology, was conducted.
In order to apply the global sensitivity analysis on these systems to aid safety design, variance based sensitivity indices are extended to stochastic processes in this paper.
The efficiency of an estimator is a measure of how reliable it is and depends on error variance (the variance not modeled by explanatory variables in the design matrix) and the design variance (a function of the explanatory variables and the contrast tested).
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Furthermore, we conducted a comparison study of privacy assessment on design variances.
These techniques, however, have relatively large design variances in the case of small sample sizes.
As bioequivalence studies are commonly conducted using crossover study designs, variance estimates are based on a within-participant coefficient of variation (CV), and effect size is determined using GMRs of selected parameters for the two formulations.
With such a design, the variance of the eluting bands can be reduced to only 30% of the variance of a single size distributor.
Then the Deff equals: (10) In a matched pair design, the variance of the differences between paired responses equals: (11) where σ2 is the variance of observations in a standard parallel group design.
In the initial design, the variance of the contact pressure, (S_{text{second}}^{2}), is 56623.4, and decreases to 6040.5 after the optimization.
In the initial design, the variance of the contact pressure, (S_{text{first}}^{2}), equals to 26872.3, and decreases to 7413.6 after the optimization.
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