পদ্ধতির তুলনা করুন

	MM-Estimation for Robust Regression	Least Median of Squares (LMS) রিগ্রেশন
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1987	1984
প্রবর্তক	Victor J. Yohai	Peter J. Rousseeuw
ধরন	Robust linear regression	Robust linear regression
মৌলিক উৎস	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.
অপর নাম	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici	LMS, least median of squares regression, en küçük medyan kareler (LMS)
সম্পর্কিত	5	5
সারসংক্ষেপ	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.