Comparar métodos

	Regresión Beta	Regresión Gamma (GLM)	Regresión Logística	Regresión Cuantílica
Campo	Estadística	Estadística	Estadística para la investigación	Econometría
Familia	Regression model	Regression model	Process / pipeline	Regression model
Año de origen	2004	1989	1958	1978
Autor original	Ferrari & Cribari-Neto	McCullagh & Nelder (GLM framework)	David Roxbee Cox	Koenker & Bassett
Tipo	Generalized linear model (beta distribution)	Generalized linear model	Method	Conditional quantile regression
Fuente seminal	Ferrari, S. L. P. & Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815.	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	beta regression model, proportion regression, Beta Regresyonu	gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM)	logit model, binomial logistic regression, LR	conditional quantile regression, regression quantiles, Kantil Regresyon
Relacionados	4	4	3	5
Resumen	Beta regression is a generalized linear model introduced by Ferrari and Cribari-Neto (2004) for outcomes that are rates or proportions confined to the open interval (0,1). It models the mean of a beta-distributed response through a link function, making it the natural choice for fractions, probability scores, and proportion indices.	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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.