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Significant variables from univariate models not being significant in the big multiple model were excluded.
All significant variables from the univariate models and the big multiple model were selected for a final model.
Variables with a crude p-value of < 0.05 in their association with LGA in the univariate model were entered into a multiple model, and variables with a p-value of < 0.05 in the multiple model were entered into the final multiple model.
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With respect to classic used multiple models, the decoupled multiple model is characterized by heterogeneous submodels in the sense that their state spaces may be of various dimensions.
A particular class of multiple model, known as decoupled multiple model, is used in order to cope with the state estimation problem of nonlinear systems.
In [15], interacting multiple model is proposed for moving target location estimation in cellular network.
Additionally, one big multiple model was fitted, including all variables.
The resulting multiple model was the main model with regard to this dependent variable.
Multiple models were then generated using Modeller.
Multiple models were constructed using different types of energy minimization and charge calculations.
Multiple models were fitted to determine the best TM parameters.
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