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Uncertainty estimation is an important part of any measurement but is often neglected for complex valued or multivariate data (e.g., vectors).
Using the Mahalanobis distance values, multivariate outliers can be identified using the χ 2 distribution.
Actual patient data was preferred over interpolated values; multivariate models constructed using the later showing no appreciable effect on the model specification or statistical inference.
*p value <0.05, percentages do not add to 100% due to missing values Multivariate analysis of selected variables and herbals and multivitamins/folic acid use among breast cancer survivors are presented in Table 3.
Because of the skewed distribution of CAC values, multivariate logistic regression analyses were employed to examine the association of depressive symptoms episodes with the presence of detectable CAC (Agatston score > 0) and high CAC (Agatston score ≥ 100).
Table 5 summarizes the information in Table 3 and Table 4 and Table 7 summarizes the information in Table 6, reporting the variables with a P-value multivariate <.05.
Data are presented as correlation coefficient (P-value) Multivariate stepwise linear regression analysis was then carried out to determine the factors associated with the CURB-65 score and PSI in both groups.
The new recurrent network architecture is driven by the interval-valued multivariate Gaussian function in the hidden node and the nonlinear Wavelet function in the consequent node.
In practice, much of time-series data consists of measuring several continuous variables simultaneously over time, which best can be considered as components of a vector-valued (multivariate) time series.
Here we present new multivariate extreme value models, as well as, coefficients to assess multivariate extremal dependence.
The survival-associated subnetworks produced by Survnet have two kinds of score network P value and multivariate Cox P value.
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