Sentence examples for model selection significance from inspiring English sources

For model selection, significance level α = 0.05 was used.

In this paper we investigated, using simulation, the performance of various strategies for model selection and significance testing for a patient prognostic variable in an EU model.

The option of model selection by significance testing of individual terms, frequently proposed in older literature, provides some guidance but is unsatisfactory in that included parameters are estimated in a way that disregards uncertainty in model choice.

Proportional hazard regression containing all clinical and pathological variables found pCR, cN3, and gender as independent prognostic factors for survival using backward elimination of non-significant variables for reduced model selection at a significance level of α = 0.05 (model (1), Table  3).> -wrap-foot> Model (1) contains all clinical and pathological covariates, as described under Methods.

Modelling was done using a strategy of selecting variables based on epidemiological knowledge and no actual model selection based on significance was done.

Prognostic factors for HBV reactivation were then determined by multivariate analysis using a stepwise logistic regression model based on a backward elimination procedure for model selection, with a significance level of <0.1 for a factor to enter into the logistic model; the same significance level was used to retain variables in the final model.

In fact model selection and statistical significance is performed within two nested K-cross validation loops as in [ 29, 30].

In fact, the model selection and statistical significance assessment are performed within two nested K-fold cross-validation loops, similarly to [ 14, 74].

Because the GEE model is not estimated by full-information maximum likelihood, the widely used tests such as the likelihood ratio test, Akaike's Information Criterion (AIC), and Bayesian Information Criteria (BIC) for model fit, penalized model selection, and block significance testing may not be appropriate.

The process will help identify relationships that exist in the data set under investigation, but will not provide information on the generalizability of the relationship to other data sets, especially if the criterion for model selection is the significance of model statistics, and cannot be used to infer causality.

The initial model consisting of all main effects was reduced by backward model selection sequentially removing non-significant effects on a 5% significance level.