The developed models are used for out of sample prediction of daily peak demand.
To do so, we simply exclude a president from the analysis, estimate the model, and then generate an out-of-sample prediction for that president.
Using this model, and including the same economic and other factors as in the original model, I can again generate an out-of-sample prediction for each president from Truman through Mr. Obama (click image to enlarge): This model's predictions are not consistently greater than actual approval.
Consistent with unsupervised clustering, PR-dependent genes were predictive of whether a particular sample was non-malignant FTE or HGSC (as indicated by a high sample prediction accuracy for each list using dataset A, total n=37 samples).
If you consider the out-of-sample predictions for all presidents, only one president consistently "beat" the prediction in his first term to an extent greater than Mr. Obama Ronald Reagann, who was about 3.3 points more popular than the prediction, on average.
This method can adjust for zero inflation (e.g. Butler and Glasbey, 2008), but does not generate direct inferences or out-of-sample predictions for the extent data themselves.
Figure 2b shows the ability for out-of-sample predictions of very long stays.
An example of out of sample prediction is shown in Fig. 2, for New York City.
In particular, the upper third of Table 1 presents averages of mean absolute percentage error (MAPE) estimates from M = 50 replications of the same out-of-sample prediction exercises for six benchmark procedures.
This gave out-of-sample prediction estimates for the expected count of cases in each stratum of the validation sample, given the model.
We then assessed the size and stability of out-of-sample prediction errors for each payer category and determined the correlation of the PCRs with traditional CCRs.
