So sánh phương pháp

	Hồi quy Poisson và Âm nhị thức	Hồi quy Bình phương Tối thiểu Thông thường (OLS)
Lĩnh vực	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	1998	2019
Người khởi xướng	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)	Wooldridge (textbook treatment); classical least squares
Loại	Generalized linear model for count data	Linear regression
Công trình gốc	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Tên gọi khác	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).