Linganisha mbinu

	Usuli wa Regresi ya Beta	Usajili wa Gamma (GLM)	Regression ya Kiasi (Quantile Regression)
Nyanja	Takwimu	Takwimu	Ekonometriki
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	2004	1989	1978
Mwanzilishi	Ferrari & Cribari-Neto	McCullagh & Nelder (GLM framework)	Koenker & Bassett
Aina	Generalized linear model (beta distribution)	Generalized linear model	Conditional quantile regression
Chanzo asilia	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Majina mbadala	beta regression model, proportion regression, Beta Regresyonu	gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM)	conditional quantile regression, regression quantiles, Kantil Regresyon
Zinazohusiana	4	4	5
Muhtasari	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.	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.