Sentence examples for model averaging approaches from inspiring English sources

Covariate balance checks for the case study showed that both Bayesian model averaging approaches offered good balance.

Both Bayesian model averaging approaches offered slightly better prediction of the propensity score compared to the Bayesian approach with a single propensity score equation.

One is model averaging approaches which make some allowance for the uncertainty in choosing an appropriate statistical model (Conigliani and Tancredi, 2009).

Kaplan and Chen (2014) approximated Bayesian model averaging approach based on the model-averaged propensity score estimates produced by the R package BMA, but which ignored uncertainty in the propensity score itself.

We utilized an AICc model selection and model averaging approach to test for potential environmental influence on these traits.

Finally, we used the same regression and model averaging approach with each of the nine individual ecosystem functions as a response variable and the set of environmental variables and the diversity metrics of all trophic levels as predictors.

This model averaging approach is more simple to compute requiring only to fit a simple multiple linear regression model, unlike the VWA approach in which the weighting is estimated for each soil property, and thus in the interests of parsimony is recommended as the model averaging technique to be adopted as protocol.

The fully Bayesian model averaging approach also provided posterior probability intervals of the balance indices.

A model averaging approach for identified peptides resulted in an AUROC of 0.86 in the validation cohort, and correctly identified virologic response in 71% of patients without the favorable IL28B "responder" genotype.

Therefore, Kaplan and Chen (2014) provided a fully Bayesian model averaging approach via MCMC to account for uncertainty in both parameters and models.

Prognostic models were developed using the Bayesian model averaging approach, and discriminative power was assessed by area under the curve.