เปรียบเทียบวิธี

	การถดถอยโลจิสติก	การประมาณค่า MM สำหรับการถดถอยที่แข็งแกร่ง (Robust Regression)	การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)	การถดถอยควอนไทล์
สาขาวิชา	สถิติการวิจัย	สถิติศาสตร์	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Process / pipeline	Regression model	Regression model	Regression model
ปีกำเนิด	1958	1987	2019	1978
ผู้ริเริ่ม	David Roxbee Cox	Victor J. Yohai	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
ประเภท	Method	Robust linear regression	Linear regression	Conditional quantile regression
แหล่งต้นตำรับ	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.	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	logit model, binomial logistic regression, LR	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
ที่เกี่ยวข้อง	3	5	5	5
สรุป	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.	The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved.	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.