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It is possible that, by giving more weight to everyday activities (i.e. household chores or work-based tasks), such regression models may reduce estimation error.
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The latter may help to reduce estimation bias due to low coverage like those reported in GBS data (e.g., see Fumagalli et al. 2013).
Similarly, for the Markov chain models, a strong association among the repeated measures or strong dependence of current observations on previous observations may reduce bias in estimation due to non-ignorable missing [ 18].
By searching for kinetic parameter values within a thermodynamically consistent subset of the parameter space, we may reduce the variance of estimation and thus lower the estimation error through the well-known bias-variance tradeoff.
However, a refinement of the exposure estimation may reduce the exposure by at least a factor of 10 to 100.
A qualitative alternative may reduce the precision in estimation but should show the effect on school-age IQ by blood lead concentration with less distortion.
Ultrasound gestational age estimation may reduce the total burden of post-term delivery by shifting the entire distribution towards younger gestational ages, but the prize seems to be paid by the small, misclassified fetus.
Therefore, the OLS estimate based on roei,t−1 may reduce the effective loss in the estimation process, which is also a motive to employ a more accurate model to forecasting returns in the first stage.
Compared with previous landmark-aided RMPCA approach, the proposed approach may reduce the mean absolute heading estimation error by 23.0% (2.05°).
However, utilizing both of these advantages in one algorithm is difficult since the randomness introduced by DE may reduce the reliability of covariance matrix estimation.
For instance, by proper designing the LDPC code and by ordering the best fractions to be retransmitted at each round may reduce the gap to the theoretical estimation.
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