قارن الطرق

	انحدار المربعات الصغرى المشذبة (LTS)	تقديرات M (الانحدار القوي)
المجال	الإحصاء	الإحصاء
العائلة	Regression model	Regression model
سنة النشأة	1984	2009
صاحب الطريقة	Peter J. Rousseeuw	Peter J. Huber
النوع	Robust linear regression	Robust linear regression
المصدر التأسيسي	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.
الأسماء البديلة	LTS, least trimmed squares regression, trimmed least squares, robust regression	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler
ذات صلة	5	5
الملخص	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.