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Indeed, while the standard random effect model has no bias, the two adjustment methods with lowest absolute bias are those using the Peters and the conditional Harbord models which were developed to circumvent the problems of the artefactual relationship.
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On the flip side, those who ranked the highest for at-risk factors tended to choose those methods with low "success" rates.
As a result, these methods lead us to methods with low cost and high-efficiency purification.
The proposed NW-Fisherfaces still outperformed other methods with low dimensionality.
Figure 7 shows objects detected by all three methods with low-threshold values.
However, the development of efficient extraction methods with low emissions is necessary.
Because of the large number of degrees of freedom, methods with low computational effort have to be used.
Therefore, there is a need for development of robust monitoring methods with low sensitivity to temperature variations.
Fig. 6 BER performances versus C/N for proposed TDE and conventional FDE methods with low-complexity reduction algorithms when the modulation method is QPSK and R D is 3% Fig. 7 BER performances versus C/N for proposed TDE and conventional FDE methods with low-complexity reduction algorithms when the modulation method is QPSK and R D is 10%.
It can also be observed from Table 12 that the proposed method TPR is highest (i.e., 0.9923) compared to the existing methods with low FPR (i.e., 0.005).
The use of adapted methods with low memory storage (Low storage Runge Kutta methods) gives good results for low precision studies, whereas the Taylor series method provides a powerful technique for high precision.
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