विधियों की तुलना करें

	एमएम-अनुमान (MM-Estimation) सघन प्रतिगमन (Robust Regression) के लिए	न्यूनतम माध्यिका वर्ग (LMS) प्रतिगमन
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model
उद्भव वर्ष	1987	1984
प्रवर्तक	Victor J. Yohai	Peter J. Rousseeuw
प्रकार	Robust linear regression	Robust linear regression
मौलिक स्रोत	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.
उपनाम	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici	LMS, least median of squares regression, en küçük medyan kareler (LMS)
संबंधित	5	5
सारांश	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.