Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Régression de Poisson et binomiale négative× | Régression quantile× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1998 | 1978 |
| Auteur d'origine≠ | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) | Koenker & Bassett |
| Type≠ | Generalized linear model for count data | Conditional quantile regression |
| Source fondatrice≠ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | 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. | 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. |
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