Comparar métodos

	Regresión Gamma (GLM)	Regresión Logística	Regresión de Poisson y Binomial Negativa
Campo	Estadística	Estadística para la investigación	Econometría
Familia	Regression model	Process / pipeline	Regression model
Año de origen	1989	1958	1998
Autor original	McCullagh & Nelder (GLM framework)	David Roxbee Cox	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Tipo	Generalized linear model	Method	Generalized linear model for count data
Fuente seminal	McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM)	logit model, binomial logistic regression, LR	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Relacionados	4	3	4
Resumen	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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.