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False Discovery Rates (FDR [48]) values were estimated as the ratio of above-threshold segmentations in a null model to above-threshold segmentations in the true distribution.

The sparse Bayesian method was employed in the model to estimate above-ground biomass (AGB) with an independent validation dataset for model validation.

AICs of modified models according to above results are shown in Table  2.

We used the same model to compare the above-ground biomass of the grass grown with the three plant classes.

In this section, we will apply the above model to the NCI/NIH colon cancer data from the SEER project.

Analyses were conducted using a similar model to that described above with the factors: risk group, time and trial segment.

The specificity of the model was tested by using the above best model to predict reading rather than math performance.

I apply the PCE model to all Cox specifications above, resulting in consistent conclusions.

Finally, we use the split-population model to test the findings above.

To achieve that goal, we changed our go-to-market model to value client relationships above all else.

We used random-effects multivariate logistic regression models to explore the above objectives.

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