विधियों की तुलना करें

	गामा रिग्रेशन (GLM)	पॉइसन और ऋणात्मक द्विपद प्रतिगमन (Poisson and Negative Binomial Regression)
क्षेत्र	सांख्यिकी	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1989	1998
प्रवर्तक	McCullagh & Nelder (GLM framework)	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
प्रकार	Generalized linear model	Generalized linear model for count data
मौलिक स्रोत	McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
उपनाम	gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM)	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
संबंधित	4	4
सारांश	Gamma regression is a generalized linear model that uses the gamma distribution to model a positive, right-skewed continuous outcome. Developed within the GLM framework of McCullagh and Nelder (1989), it is an alternative to ordinary linear regression for variables such as health-care costs, durations, and income.	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.