Comparer des méthodes

	Régression quantile	Régression de Poisson et binomiale négative
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1978	1998
Auteur d'origine	Koenker & Bassett	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Conditional quantile regression	Generalized linear model for count data
Source fondatrice	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	conditional quantile regression, regression quantiles, Kantil Regresyon	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Apparentées	5	4
Résumé	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.	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.