विधियों की तुलना करें

	सामान्यीकृत रेखीय मॉडल (GLM)	ऋणात्मक द्विपद समाश्रयण (Negative Binomial Regression)
क्षेत्र	सांख्यिकी	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1972	2011
प्रवर्तक	John A. Nelder & Robert W. M. Wedderburn	Hilbe (textbook treatment); generalized linear model framework
प्रकार	Regression framework	Generalized linear model for count data
मौलिक स्रोत	Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
उपनाम	GLM, generalized regression, exponential family regression, link-function model	NB regression, NB2 regression, negatif binom regresyonu
संबंधित	6	4
सारांश	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.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.