Sentence examples for backwards stepwise selection likelihood from inspiring English sources

The same result was obtained using backwards stepwise selection (likelihood ratio).

The minimum adequate model was then determined through manual backwards stepwise selection, using maximum likelihood methods.

Once the appropriate random component had been determined, the fixed component of the model was refined by manual backwards stepwise selection using maximum likelihood (ML) to remove insignificant independent variable terms.

We then used backwards stepwise selection with a maximum p-value set at 0.05 to select the independent variables to include in each final model while simultaneously avoiding possible colinearity between variables.

Variables were selected for inclusion into the final logistic regression model for ARDS development using a backwards stepwise selection algorithm at a threshold value of P>.2.

Backwards stepwise selection was used to refine the model so that only variables with p-values ≤.01 were retained in the model.

Logistic regression with backwards stepwise selection was then used with the 20 tagged SNPs and covariates to control for population stratification, i.e. sex and dataset (discovery versus replication).

Variables with p ≤ 0.05 were included into multivariate Cox regression analysis with backwards stepwise selection.

Model 1 was built using backwards stepwise selection until only covariates significant at p < 0.05 remained.

A backwards stepwise selection method identified those variables that were independently associated with successful self management.

After stepwise selection of the main terms, we added interaction terms between the remaining explanatory variables, and further refined the models through backwards, stepwise selection.