方法对比

	MM估计量稳健回归	最小中位数平方（LMS）回归
领域	统计学	统计学
方法族	Regression model	Regression model
起源年份	1987	1984
提出者	Victor J. Yohai	Peter J. Rousseeuw
类型	Robust linear regression	Robust linear regression
开创性文献	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.
别名	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici	LMS, least median of squares regression, en küçük medyan kareler (LMS)
相关	5	5
摘要	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.	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.