multinomial


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multinomial

[¦məl·tə¦nō·mē·əl]
(mathematics)
An algebraic expression which involves the sum of at least two terms.
References in periodicals archive ?
Correct and incorrect responses from the IAT were then modeled with the quadruple process or quad model (Sherman et al., 2008), which is a multinomial processing tree model that has been validated across a variety of tasks, including the IAT (Conrey et al., 2005; Sherman et al., 2008).
To verify our model and to verify the difference between cross projects and single project, we compare it with the Naive Bayes Multinomial approach; the Lamkanfi's work [11, 12], which is a well-known prediction method; and an emotional similarity-based severity method [13].
We then calibrate probabilities using multinomial logistic regression, a common means of calibrating three or more probabilities to a national total (8) that applies constant odds ratios rather than a constant arithmetic difference, allowing well-predicted probabilities to increase more than proportionately.
We fit the proportion of normal seedlings, and abnormal seedling in conjunction with non-germinated seeds to the distribution binomial and beta-binomial, and next, we fit the proportion of normal seedlings, abnormal seedlings, and non-germinated seeds to the multinomial distribution following the design in Figure 4.
To address this question, I run the same logit and multinomial logit regressions that are shown in panel A of table 3 for classification schemes 2 and 3.
Keywords: Local product, consumer, multinomial logit, purchase place, Turkey.
(9) Specifically, the FMLogit model assumes that the M conditional means have a multinomial logit functional form in linear indexes as
We used multinomial logistic regression when the predictor variables are nominal.
Due to three discrete outcomes of the dependent variable, the multinomial logistic regression (MNL) was selected for the modelling purpose, as adopted by many previous studies in this field [25-28].
The logit and probit qualitative choice models can also be extended to multinomial approaches, in which the dependent variable can take one of several attributes in order to cover a greater number of possible occurrences for the studied phenomenon.
By comparison, the Multinomial Naive Bayes model is found to outperform the random forest model, so it is chosen as the classification algorithm for identifying online users in a sliding window manner.
To do so, we modeled the risk surface using a multinomial generalized additive model (GAM).