So sánh phương pháp

	Bình phương tối thiểu có trọng số mạnh mẽ (Robust WLS)	Hồi quy Bình phương Tối thiểu Thông thường (OLS)	Hồi quy Quantile
Lĩnh vực	Kinh tế lượng	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model	Regression model
Năm ra đời	1964/1981	2019	1978
Người khởi xướng	Huber, P. J.	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
Loại	Robust weighted regression	Linear regression	Conditional quantile regression
Công trình gốc	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	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.
Tên gọi khác	robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
Liên quan	5	5	5
Tóm tắt	Robust WLS combines weighted least squares — which corrects for known or estimated heteroscedasticity — with robust M-estimation that down-weights influential outliers. The result is a regression estimator that is simultaneously efficient under non-constant error variance and resistant to observations that would otherwise distort coefficient estimates.	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.