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In addition, two different mixture methods are tested; the first is to add SSPCM to the concrete mixture, and the second is to replace the fine aggregate with the same amount of SSPCM.
In this situation, mixture methods are redundant since the distinction between infected and uninfected children is easily made and there are few children with intermediate results.
Mirror or mixture methods are therefore of limited value in this setting.
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The mixture methods were also able to estimate the effect magnitude with a lower imprecision than the EXTRA method (lower relative standard errors, RSE).
To minimize the number of experiments, mixture method was used as an appropriate tool for experimental design.
To minimize the number of experiments, mixture method was used as an effective tool for experimental design.
However, as suggested by results with 'poor' prior (M6), when there is a large difference in exposure between the data sets used to model exposure and true exposure being predicted, the Bayesian normal mixture method is expected to falter relative to the simple collection of relevant data.
Until now, most of the proposed mixture design methods are based on a trial and error procedure, which is expensive and work intensive.
In this section, the MSE performances of the proposed LOS/NLOS mixture source localization methods are compared with those of the M-estimator [16] and LMedS estimator [28].
In a strict quantitative sense, however, this can be done only for the tested mixture concentrations; because extrapolation methods are unsuitable, the mixture data cannot be figured out using the same concentration scale.
However, similar estimates were obtained for θAsian when using any of the mixture methods, but were estimated to be about 30% higher than when using the EXTRA method.
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