Comparer des méthodes

	Régression logistique multinomiale	Régression de Poisson et binomiale négative
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1974	1998
Auteur d'origine	McFadden	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Multinomial logistic regression	Generalized linear model for count data
Source fondatrice	McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Apparentées	5	4
Résumé	Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.	Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.