Sammenlign metoder

	MM-estimering for robust regression	Least Median of Squares (LMS) Regression
Fagområde	Statistik	Statistik
Familie	Regression model	Regression model
Oprindelsesår	1987	1984
Ophavsperson	Victor J. Yohai	Peter J. Rousseeuw
Type	Robust linear regression	Robust linear regression
Oprindelig kilde	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.
Aliasser	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici	LMS, least median of squares regression, en küçük medyan kareler (LMS)
Relaterede	5	5
Resumé	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.