เปรียบเทียบวิธี

	Gamma Regression (GLM)	การถดถอยโลจิสติก	การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)	การถดถอยควอนไทล์
สาขาวิชา	สถิติศาสตร์	สถิติการวิจัย	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Process / pipeline	Regression model	Regression model
ปีกำเนิด	1989	1958	2019	1978
ผู้ริเริ่ม	McCullagh & Nelder (GLM framework)	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
ประเภท	Generalized linear model	Method	Linear regression	Conditional quantile regression
แหล่งต้นตำรับ	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.	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.
ชื่อเรียกอื่น	gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM)	logit model, binomial logistic regression, LR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
ที่เกี่ยวข้อง	4	3	5	5
สรุป	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.	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.