เปรียบเทียบวิธี

	Generalized Linear Model (GLM)	การถดถอยพัวซงและทวินามเชิงลบ
สาขาวิชา	สถิติศาสตร์	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	1972	1998
ผู้ริเริ่ม	John A. Nelder & Robert W. M. Wedderburn	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
ประเภท	Regression framework	Generalized linear model for count data
แหล่งต้นตำรับ	Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
ชื่อเรียกอื่น	GLM, generalized regression, exponential family regression, link-function model	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
ที่เกี่ยวข้อง	6	4
สรุป	The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case.	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.