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The rate of uncertainty arising from the choice of RCM, driving GCM, and radiative concentration pathway (RCP4.5 & RCP8.5) is quantified using a variance decomposition technique after reconstruction of missing data in GCM × RCM combinations.
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We apportioned the sources of uncertainty in excess mortality estimates using a variance-decomposition method.
Finally, we attributed the uncertainties in the excess mortality estimates to various factors using a variance-decomposition method.
Using a variance component decomposition approach, the latter method allows flexibility in handling complex data sets.
A variance decomposition approach is used to calculate the total effect of each uncertain variable.
We conducted a variance decomposition on the total variance of TEE from this model using estimated mean squares from a Type I ANOVA Table, calculated using the proc mixed procedure from SAS [ 28], and methods described in Searle et al. [ 44].
A variance decomposition sensitivity analysis method, Analysis of Variance (ANOVA), is used for sensitivity quantification, because it is capable of handling small samples and more computationally efficient compared with other approaches.
Modelling the item data in a variance decomposition analysis allows the separate evaluation of model fit regarding the measurement model and the variance decomposition model.
An analysis treating the sum scores as bivariately normal and applying a variance decomposition will approximate the true proportions.
Identification of the key parameters using generalized variance decomposition (Sobol Indices) by means of a global sensitivity analysis.
(iii) We combined all variables into a single multiple regression analysis in order to examine the effects of all of these simultaneously and analyzed the effects using a sequential decomposition of variance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com