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The characteristics of the ELO model simplification to its original model are revealed.
Inferences were drawn from the full models (all predictors present) [ 45] and also after model simplification to confirm the stability of the models.
Once the significance of the full model has been established, one may want to proceed with model simplification to narrow down the significant predictors.
This also implies that if one applies model simplification to increase the confidence in 'important' estimates, there actually is not much to be gained from model simplification for large datasets.
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The aim of model simplification is to eliminate parts of a model that are unimportant for the properties of interest.
Problematically, model simplification tends to disguise the multiple-testing problem.
Model simplification leads to a model with generation time and fecundity remaining.
The aim of the model simplification is to reduce all scales that are faster than this chosen scale.
But the number of degrees of freedom is huge and some model simplification is required to compute a whole stack.
A systematic approach is proposed in this paper for model simplification in order to reduce the number of state variables and parameters.
Stepwise model simplification then helped to identify the minimal adequate model that necessitates the lowest number of parameter estimates [46].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com