Сравнение методов

	Гамма-регрессия (Обобщенная линейная модель)	Регрессия отрицательного биномиального распределения	Регрессия методом обыкновенных наименьших квадратов (ОНМК)
Область	Статистика	Эконометрика	Эконометрика
Семейство	Regression model	Regression model	Regression model
Год появления	1989	2011	2019
Автор метода	McCullagh & Nelder (GLM framework)	Hilbe (textbook treatment); generalized linear model framework	Wooldridge (textbook treatment); classical least squares
Тип	Generalized linear model	Generalized linear model for count data	Linear regression
Основополагающий источник	McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Другие названия	gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM)	NB regression, NB2 regression, negatif binom regresyonu	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Связанные	4	4	5
Сводка	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.	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).