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In simulations where undervaccinated groups were included in addition to temporal changes, model selection performed optimally when the absolute difference in coverage was greater than 20%.
Multivariable logistic regression analysis was used to determine factors associated with willingness to undergo a kidney transplant, with model selection performed utilizing backward elimination.
These posterior probabilities give the inferred statistical support for each mechanism, derived from model selection performed with approximate Bayesian computation (ABC) (Beaumont et al., 2002; Toni et al., 2009; Sunnåker et al., 2013; Johnston, 2014) using uniform priors.
These models can then be compared, and model selection performed, with a likelihood ratio test, or in this case the difference in each model's deviance, which is equivalent to a likelihood ratio test for nested models.
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Backward model selection was performed to select risk factors for the final model.
Then statistics and model selection were performed.
On the second stage, acoustic model selection is performed.
Parameter joint inference and model selection are performed under the Bayesian paradigm.
Model selection was performed using the Akaike information criterion (AIC, Akaike 1973) in conjunction with a backward elimination procedure.
Model selection was performed by comparing the AIC values (Akaike Information Criterion) and the relative weight of each model (wAIC).
Model selection is performed on the inner cross-validation loop, and the predictivity estimate of the selected model is evaluated with the outer cross-validation loop.
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