Σύγκριση μεθόδων

	Παλινδρόμηση Beta	Παλινδρόμηση γάμμα (GLM)	Λογιστική Παλινδρόμηση
Πεδίο	Στατιστική	Στατιστική	Ερευνητική Στατιστική
Οικογένεια	Regression model	Regression model	Process / pipeline
Έτος προέλευσης	2004	1989	1958
Δημιουργός	Ferrari & Cribari-Neto	McCullagh & Nelder (GLM framework)	David Roxbee Cox
Τύπος	Generalized linear model (beta distribution)	Generalized linear model	Method
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	beta regression model, proportion regression, Beta Regresyonu	gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM)	logit model, binomial logistic regression, LR
Συναφείς	4	4	3
Σύνοψη	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.