Vertaile menetelmiä

	Gamma-regressio (GLM)	Logistinen regressio	Negatiivinen binomiregressio
Tieteenala	Tilastotiede	Tutkimuksen tilastomenetelmät	Ekonometria
Menetelmäperhe	Regression model	Process / pipeline	Regression model
Syntyvuosi	1989	1958	2011
Kehittäjä	McCullagh & Nelder (GLM framework)	David Roxbee Cox	Hilbe (textbook treatment); generalized linear model framework
Tyyppi	Generalized linear model	Method	Generalized linear model for count data
Alkuperäislähde	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.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
Rinnakkaisnimet	gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM)	logit model, binomial logistic regression, LR	NB regression, NB2 regression, negatif binom regresyonu
Liittyvät	4	3	4
Tiivistelmä	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.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.