Comparer des méthodes

	Régression logistique ordinale (modèle des cotes proportionnelles)	Régression de Poisson et binomiale négative
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	2010	1998
Auteur d'origine	Agresti (textbook treatment); proportional odds model	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Ordinal logistic regression	Generalized linear model for count data
Source fondatrice	Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	proportional odds model, ordered logit, ordinal logistic regression, Ordinal Regresyon (Proportional Odds)	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Apparentées	5	4
Résumé	Ordinal logistic regression models an ordered categorical outcome — such as a Likert rating, a satisfaction level, or an education tier — as a function of predictors. It is the ordinal extension of logistic regression, developed in standard treatments such as Agresti's Analysis of Ordinal Categorical Data (2010), and in its most common form it is the proportional odds model.	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.