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In addition, if the auxiliary variables are also predictive of censoring, the analyses using the information from the auxiliary variables can reduce bias due to dependent censoring.
The current results suggest that using such techniques to account for attrition related to baseline variables can reduce the negative effects of selective attrition on regression estimates even if these techniques do not account for attrition related to follow-up variables.
Including such variables, which are highly correlated with the missing variables, can reduce the bias that might otherwise be generated by the missing data (CASP-19 in 2009 and 2005 were correlated at 0.74; mental health in 2003 and 2007 at 0.61; physical functioning in 2003 and 2007 at 0.60) (Collins, Schafer, & Kam, 2001).
This definition implies that the information that Y provides about X reduces uncertainty about X due to the knowledge of Y. Intuitively, mutual information infers the information that Y and X share by measuring how much knowing one of the variables can reduce the uncertainty about the other [ 25].
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Several social programs have been used with success, as they do not address endogenous variables that can reduce efficiency, like the Cultural Factor.
Design of experiments is a statistical procedure focused on detecting these links between the working variables and can reduce significantly the number of experiments, keeping, however, the reliability of the conclusions at a high standard.
The purpose of this section is to apply a new method to investigate the stability of system (4.1), which combined with the "freezing technique," will allow us to derive explicit estimations to their solutions, namely, introducing new variables; one can reduce system (4.1) to a delay-free linear difference system of higher dimension.
Boolean variables [19] can reduce the complexity, although the coarse-graining will limit predictability to qualitative characteristics such as bifurcations.
The advantages of using SaTScan are that it can adjust for confounding variables, it can reduce pre-selection bias as it searches for clusters without specifying their size or location, it gives a single p-value as the likelihood-ratio-based test takes account of multiple testing, and finally, it can be applied to a whole region to detect significant clusters in that region (10).
The adjoint variable method can reduce computation time and save computer resources because it can provide the sensitivity values only at the positions in which designers are willing to obtain.
Thus, to address this methodological issue we used costs after infection as dependent variable, which can reduce cost overestimation due to time and length bias.
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CEO of Professional Science Editing for Scientists @ prosciediting.com