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Granted, the baseline model can be a panacea to detect and modify relatively sparse outliers, yet the model based method can be detrimentally affected when the amount anomalous load points increases.
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Specifically, the term representing change in baseline hazard in the model can be subsumed into the vector of parameters as a dummy variable that represents change in hazard [ 25], making these models straightforward to fit.
If attrition is significantly related to one or more baseline characteristics, the predicted values from this model can be used as a covariate to adjust for differential attrition.
If carbon values are assigned to the changes in different forest classes, the resulting change of the model can be translated into locally differentiated baseline emissions.
This model can be quantified.
The model can be changed.
Your role model can be anyone.
Methods including change-from-baseline comparison, covariance analysis, and regression models can be used to adjust for baseline differences, but there were not enough data from the included studies to conduct a meta-analysis.
These models can be used for denoising or for the suppression of an unwanted baseline wander (detrending) of the signal of interest.
Furthermore, models can be compared in a hierarchical fashion, i.e., starting from a baseline model, all further models can be specified as special cases defined by restrictions on the parameters of the baseline model.
Models can be another issue.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com