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The goodness of fit Bayesian Information Criteria statistic (SBIC) was used to assess the need of a separate covariance matrix for each treatment.
Significant variables (p < 0.05) or variables that improved the fit of the model based on the Akaike information criteria statistic were retained in the model.
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First, the Bayesian information criterion statistic for a solution with k classes should be lower than for a solution with k-1 classes, suggesting that adding additional classes improves model fit.
Since the number of differentially expressed genes for σ wtrep is increased for the same cutoff criteria, this statistic exhibited a higher statistical power.
> -wrap-foot> tumoror, BCI Breast Cancer Index, CI confidence interval a p Value is based on the likelihood ratio criterion likelihood ratio criterion test statistic has approximately a chi-square distribution with 1 degree of freedom.
Goodness of fit was assessed with the Akaike Information Criteria (AIC) statistic [ 39].
The best combination of covariates, which was those with the lowest value of the Bayesian Information Criteria (BIC) statistic, [ 36] was selected for the prediction of malaria risk.
In this study, agreement between readers was 68% for the modified IHP criteria (κ statistic =0.445) and 71% (κ = 0.502) for the Deauville criteria, demonstrating only a moderate level of agreement in interpretation, even between expert readers.
Estimation and rANOVA model optimization were based on a maximum-likelihood procedure using the Akaike information criterion (AIC) statistic.
The best-fitted model was selected from likelihood ratio (LR) tests and Akaike information criterion (AIC) statistic.
The selection of scales and number of degrees of freedom for the baseline spline function was made based on the Bayes information criterion (BIC) statistic.
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