방법 비교

	최소 중앙값 제곱합 (Least Median of Squares, LMS) 회귀분석	조건부 분위수 회귀
분야	통계학	계량경제학
계열	Regression model	Regression model
기원 연도	1984	1978
창시자	Peter J. Rousseeuw	Koenker & Bassett
유형	Robust linear regression	Conditional quantile regression
원전	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
별칭	LMS, least median of squares regression, en küçük medyan kareler (LMS)	conditional quantile regression, regression quantiles, Kantil Regresyon
관련	5	5
요약	Least Median of Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of minimising the sum of squared residuals like ordinary least squares, it minimises the median of the squared residuals, which lets the fit resist contamination by up to roughly 50% outliers.	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.