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Model selection criterion.
Because of the small number of samples considered here and to avoid overfitting we used a standard model selection procedure to select population-specific reference signals to include in the fit of each gene.
Standard model selection procedures were used to select the final models 34.
In such a case, most probe sets will interrogate non-polymorphic sequence and thus be uninformative, but standard model selection procedures (e.g. Bayesian information criterion (BIC)) appear to be sufficient for identification of sets which exhibit two-class behavior.
We used standard model selection procedures [ 50] for acoustic parameters.
We compared these two models to determine whether adding the new variable significantly increased the fit of the model to the data using standard model selection techniques.
This work first uses standard model selection criteria to demonstrate that the beta distribution error rate model improves inference within the capture recapture framework.
Whenever probabilistic models exist, we can employ standard model selection approaches of either a frequentist, Bayesian or information theoretic nature (Burnham and Anderson, 2002; Vyshemirsky and Girolami, 2008).
Two are the standard model selection criteria of model error and simplicity and three are based on the expected behavior of hidden system variables.
The 'randomized lasso' decorrelates variables and therefore addresses the model selection inconsistency problem of standard lasso in the presence of correlations between relevant and irrelevant variables.
Formal model selection has become a standard practice for choosing an inferential model and works well for comparing models of a specific type, such as comparisons within nucleotide models or within amino acid models.
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