Sentence examples for stepwise prediction from inspiring English sources

Furthermore, the infarct volumes predicted by the enter and stepwise prediction models were consistent with the observed values.

Multivariate Cox regression analysis of time to EFS excluded the variables of patient age, MYCN status, and 11q status in a stepwise prediction model of survival time, leaving only INSS stage 4 vs stages 1, 2, 3, 4S and >/< median miR-204 expression as significant contributing variables (Table 1).

Combining the factors from both the MOS social support survey and the SF-36 in a stepwise prediction model, we found that only four factors, namely tangible support, bodily pain, vitality, and mental health were significant predictors of reporting depressive symptoms, defined as a BDI score of ≥10 (Table 4).

Hence, the VI based ranked variables can be feed to any machine learning algorithm to build the stepwise predictive models to find a better balance between the biologically relevant set of features and prediction error (RMSE).

Of the candidate algorithm considered for prediction, stepwise regression AIC had the lowest cross-validated mean squared prediction error.

In model 1, the FIM + FAM total admission score, and NIS physical and cognitive scores on admission all met the criteria for entry and were entered stepwise into the prediction model.

The SOC estimation using wavelet reconstruction based on predicted SOC at all scale components outperformed its prediction using stepwise multiple linear regression (SMLR) based on the original sampling data.

Fig. 3 Designing learning activity with instructional strategy design components mapped to objective Fig. 4 Stepwise implementation plan of "Prediction with PV".

For the limited sample, zeroth-order systems built with hybrid training were found to outperform both first-order systems and stepwise regression models in prediction accuracy, while sensitivity analyses confirmed their robustness.

Dai et al. [ 12] proposed a SNP-Haplotype Adaptive Regression (SHARE) algorithm that seeks the most informative set of SNPs for genetic association in a targeted candidate region by growing and shrinking haplotypes with one more or less SNP in a stepwise fashion, and comparing prediction errors of different models via cross-validation.

Crossvalidation, an example of an internal validation procedure used here, involves removing observations from the data in a stepwise procedure, computing a prediction model based on the remaining samples and testing the calculated model by comparing the estimated values with the empirically obtained values for the excluded observations.