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Missing values will be replaced using multivariate imputation techniques.
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Missing covariate data were replaced using multiple imputation (imputed datasets: N=30) applying multivariate chained equations as described for use in the Cox model.
Missing values were replaced using multiple imputation.
Missing values were replaced using linear extrapolation.
Missing ranks were then replaced using multivariate imputation by chained equations (MICE; van Buuren & Groothuis-Oudshoorn, 2011; van Buuren & Oudshoorn, 2000).
In effect, the system of coupled ordinary differential equations can be replaced by multivariate linear models, which can be used as surrogate models.
They also must be replaced after use.
In test constructions, the univariate ranks are replaced by multivariate spatial ranks (J. Nonparam. Statist. 5 (1995) 201).
Under the framework of latent variable models, the commonly adopted multivariate Gaussian distribution assumption is replaced by the multivariate student t-distribution so as to tolerate the notorious outliers by using the adjusted heavy tail.
They can be reduced by using multivariate statistics.
GFP was replaced by mCherry using AgeI and BsrGI.
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