processing trees as theoretical bridges between cognitive and social psychology.
* We compare our approach with the Naive Bayes Multinomial
approach; the Lamkanfi methodology, which is a well-known severity prediction approach; and an emotional similarity-based bug severity approach.
We used multinomial
logistic regression in MBISG 2.0 so that we could incorporate both new and prior data elements outside of the Bayesian framework.
logistic regression analysis is a method which is used to determine the relationship between the response variable and explanatory variables (independent variables) in cases where the response variable has three or more categories.
As with the multinomial
logit estimator, one needs to normalize for identification purposes, and we choose the normalization [[beta].sub.M] = 0.
We used three types of analyses of multiple regression, SEM and multinomial
logistic regression to identify behavioral factors and motivational factors.
In the current paper, two multinomial
logistic regression models were established for HR and NHR crash data, respectively.
 extract user's destination domain names and the corresponding visiting frequency to derive the behavioral fingerprints and use Multinomial
Naive Bayes classifier to classify them.
We evaluated the effect of time in the multinomial
GAM by adding year to the model as a continuous variable, testing the effect of year as both a linear term and as a smoothed term using a thin plate spline.
logit methodology was adopted.
Factors that adversely influenced pregnancy outcomes, including lupus nephritis, anti-Ro/SSA antibody, anti-La/SSB antibody, C3 and C4, antiaCL antibody, recurrence of lupus, hypertension, and Raynaud's phenomenon, were analyzed using multinomial
Therefore, we i) generated the additional set of breed-offsetting SNPs (boSNPs), ii) selected the best features through LASSO multinomial
logistic regression, and iii) evaluated the highest-scored features using multiclass classifiers in the present study.
This categorical variable was then regressed against the six independent variables described above using multinomial