Sentence examples for model selection that from inspiring English sources

Analysis was also performed using a model selection that included all covariates.

Liebminger et al. proposed in 2007 [ 19] an algorithm for model selection that combined forward variable selection and all subsets regression.

As the goal is to detect reliable QTL, most discussion will be focused on results from the final fit of the model selection that is conditioned by the Bayesian information criterion (BIC) [ 17].

The AIC is a method of model selection that allowed for comparing non-nested models, in this case, comparing a model with GA to a model with days to MRI, while penalizing for model complexity.

In addition, this type of multimodel inference method avoids some of the common issues of stepwise model-selection methods, including the inconsistencies in model selection that result from different stepwise methods and criteria (reviewed in Hegyi and Garamszegi, 2011).

We compared the variables included in the final model by either using model selection that did not consider diagnostic outcome, or by retaining diagnostic outcome in the model throughout variable selection.

We developed a nonlinear model regression model selection technique that accommodates variables that differ only by a hyperparameter and can be adapted for various nonlinear model forms.

A simple and computationally wise low demanding approach is here proposed for parameter estimation and model structure selection that provides an answer to the mentioned issues.

We would want to have a model selection method that enables to select the adjacency matrix that best predicts the true topology.

A key component of our algorithm is the model selection method that selects the appropriate number of clusters by adjusting the model complexity 'control knob' α b.

We describe an alternative model selection strategy that identifies which hypothesis, selected from an a priori-compiled set of hypotheses, explains the most variation in the outcome.