方法对比

	Gamma回归（GLM）	普通最小二乘法 (OLS) 回归	分位数回归
领域	统计学	计量经济学	计量经济学
方法族	Regression model	Regression model	Regression model
起源年份	1989	2019	1978
提出者	McCullagh & Nelder (GLM framework)	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
类型	Generalized linear model	Linear regression	Conditional quantile regression
开创性文献	McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
别名	gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
相关	4	5	5
摘要	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.	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).	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.