Abadie et al. (2015, p. 502): "Intuitively, the cross-validation technique selects the weights v m that minimize out-of-sample prediction errors".
In a practical situation, the embedding dimension (d) and the number of nearest neighbours (k) are estimated by minimizing the in-sample prediction errors, where (din left{ {2,3,ldots,8} right} ) and (kin left{ {1,2,ldots,20} right} ) is the percentage of neighbours with respect to the sample size.
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.
AIC, defined as deviance plus 2 times the number of predictors, measures the predictive power; a model is estimated to reduce out-of-sample prediction error if AIC decreases.
Although the predicted series are convergent, with the increasing of the testing samples' number, prediction errors have a tendency to increase Fig. 3 Prediction results using single-RBF predictor with data start point at 1st.
In Fig. 2, with the increase of the testing samples' number, prediction errors are tended to increase.
Also, decreasing sample size increased prediction errors considerably in some cases, especially for the LARG strategy.
To evaluate the performance of a chaotic modeling approach of sea clutter, prediction errors and predictable samples' number are two commonly used indicators [18].
Further, we note that these are in-sample errors rather than expected prediction errors estimated by out-of-sample forecasting.
Prediction errors for each sampling strategy are shown in Table 2, for the scenario of sampling 12 hospitals.
Increasing the sample size did not affect the overall ranking of the sampling strategies by prediction error; the scenarios n = 12, n = 18 and n = 6 all resulted in a preference for MAXVAR.
