方法对比

	稳健回归	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	1964	2019	1978
提出者	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
类型	Regression with outlier resistance	Linear regression	Conditional quantile regression
开创性文献	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.	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	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
相关	6	5	5
摘要	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.	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.