Сравнение методов

	Регрессия по методу наименьших усеченных квадратов (LTS)	M-оценки (робастная регрессия)	MM-оценка для робастной регрессии
Область	Статистика	Статистика	Статистика
Семейство	Regression model	Regression model	Regression model
Год появления	1984	2009	1987
Автор метода	Peter J. Rousseeuw	Peter J. Huber	Victor J. Yohai
Тип	Robust linear regression	Robust linear regression	Robust linear regression
Основополагающий источник	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.
Другие названия	LTS, least trimmed squares regression, trimmed least squares, robust regression	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici
Связанные	5	5	5
Сводка	Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers.	M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit.	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.