This model selects those that make the top 25 under our Balanced Fundamentals ranking system.
For the final analysis, the model selected was that accounting for the greatest variance (adjusted R).
All other factors being equal, each 'best model' selected was that with the strongest pseudo R-square statistics.
The final model selected was that which maximized explained variance as indicated by Akaike's information criterion (AIC; where smaller is better).
By default, the top-ranked model is selected that corresponds to the maximum likelihood criterion for model selection; it only takes into account the least-squares fit to the observed data.
Estimates of relative risk from a crude regression model unadjusted for poverty were consistently lower than for the regression model we selected that did adjust for poverty (Additional file 1: Figure S4).
A key component of our algorithm is the model selection method that selects the appropriate number of clusters by adjusting the model complexity 'control knob' α b.
This finding is not indicative that out-of-pocket is not important for MMR, or that total health expenditure is not important for IMR and CMR, but rather that the model selected the variable that described the strongest association.
When a big portion of the scene is suddenly detected as FG, the best model is selected, that is, the one that minimizes the amount of FG pixels.
An overall best model was selected that had the maximum accuracy in the testing data (i.e. testing accuracy or TA).
