multinomial

Allen and Sherman (2011) used a multinomial model to disentangle the activation of negative information toward out-groups and the inhibition of such activation, in which they found that there is no attitudinal regulation following negative feedback.

Then, we extract the bug reports belonging to the selected topics and apply Naive Bayes Multinomial [11] to these.

The second phase uses a more flexible framework, multinomial logistic regression, which allows for both additional types of predictors of race/ethnicity and relaxation of the conditional independence assumption used in the application of Bayesian updating used in MBISG 1.0.

To determine this, I run a multinomial logit where the possible outcomes are the three transitions just mentioned and no transition at all.

In order to analyze the data obtained, a multinomial logit model was employed.

On multinomial regression analysis, the researchers found that carrying naloxone often or always was significantly associated with the following variables: female sex (odds ratio, 2.77), having ever witnessed an overdose (OR, 1.84), having injected in the past 12 months (OR, 1.75), and having ever used naloxone during an overdose (OR, 4.33).

We assume that the relationship between payment shares and explanatory variables is captured by a fractional multinomial logit (FM-Logit) model, which states the expected share of each payment type, conditional on the explanatory variables, is a multinomial logit function of the explanatory variables:

A three-phase examination covering Structural Equation Modeling (SEM), numerous relapse, and multinomial strategic relapse is utilized.

A wide variety of methodological approaches were discussed, including binary outcome models, ordered discrete outcome models, unordered multinomial discrete outcome models, and other data mining approaches.