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Therefore, the role of StB is examined in more detail in the multivariate setting.
For a reliable analysis and accurate results, these assumptions should also be checked prior to modeling in the multivariate setting.
In the multivariate setting (say, (mathbb {R}^{d})), let U d ∼N d (0,I d ) be the d-dimensional normal distribution with I d as the d×d identity matrix.
The partial PVE was calculated to indicate relative importance of the individual predictor in the multivariate setting: The combined-PI risk predictor captured 11.3% of the variability compared with 5% captured by tumor size.
Hence, the variable was also considered in the multivariate setting.
All variables significant at the 10% level on univariate analysis were considered further in the multivariate setting.
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The aim of this subsection is to extend some results in Section 2 to the multivariate setting.
The multivariate setting in the MCSF test appears to be sensitive for detecting motor-activating effects of low doses of alcohol as well as reduced locomotion at doses lower than in other behavioral tasks.
Unfortunately, in this multivariate setting, the verification of the above assumptions is often neglected.
The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark.
The Cox proportional-hazards regression model was used to study in a multivariate setting the effect of MYCN status (amplified, not amplified) as potential confounder.
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