Sentence examples for deterioration of the model from inspiring English sources

Thus, the majority of identified data for this threshold suggested that outliers could just appear due to the statistical properties of the data and their removal can lead to deterioration of the model quality.

To get an indication of the statistical power to detect the genetic variant in the two designs, we studied the deterioration of the model fit, expressed as increase in χ2, when the association between the genetic variant and the operationalisation of the trait (sum-score or latent factor) was fixed to 0, i.e., a test with 1 degree of freedom (df).

However, equating thresholds in two steps (first within men, then within women) did not result in a significant deterioration of the model fit.

Constraining the variance components to be equal across same and different teachers also resulted in a significant deterioration of the model fit.

If the difference test is significant (threshold for the genetic models p < 0.01 due to multiple testing) the constraints on the nested model cause a significant deterioration of the model.

For the model to make sense, we constrained this correlation to 1, all twin correlations to be equal across the two Linear slope factors, and the cross-trait-cross-twin correlation to equal the twin correlation, even though this resulted in a slight deterioration of the model fit (χ(5) = 14.55, P < .01).01

Fixing the dominance effects of the Intercept factor to zero (i.e., AE model) did not result in a significant deterioration of the fit (Model 9 vs. Model 8: χ(1) < 1, ns), but fixing both the additive genetic effects and the dominance effects to zero (i.e., E model) did (Model 10 vs. Model 8: χ(2) = 21.16, P < .001).001

Fixing the common environmental effects of the Intercept factor to zero (i.e., AE model) did not result in a significant deterioration of the fit (Model 9 vs. Model 8: χ(1) < 1, ns), and neither did fixing the additive genetic effects to zero (i.e., CE model: Model 10 vs. Model 8: χ(1) = 1.59, ns).

This, however, resulted in a significant deterioration of the fit (Model 2 vs Model 1c: χ(9) = 18.00, P < 0.05).

This is likely due to the lack of accurate global data close to the model endpoints, forcing modellers to apply unrealistic endpoint conditions that can result in a deterioration of the quality of model secular variation descriptions close to the endpoints.

The noise in X and Y might be the source of the deterioration of the SACFM-2 model at ground level in these components.