방법 비교

	Robust Regression	조건부 분위수 회귀
분야	통계학	계량경제학
계열	Regression model	Regression model
기원 연도	1964	1978
창시자	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)	Koenker & Bassett
유형	Regression with outlier resistance	Conditional quantile regression
원전	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
별칭	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation	conditional quantile regression, regression quantiles, Kantil Regresyon
관련	6	5
요약	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.	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.