So sánh phương pháp

	Hồi quy Poisson và Âm nhị thức	Hồi quy Quantile
Lĩnh vực	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	1998	1978
Người khởi xướng	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)	Koenker & Bassett
Loại	Generalized linear model for count data	Conditional quantile regression
Công trình gốc	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Tên gọi khác	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon	conditional quantile regression, regression quantiles, Kantil Regresyon
Liên quan	4	5
Tóm tắt	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.