Methoden vergleichen

	Least Trimmed Squares (LTS)-Regression	M-Schätzer (Robuste Regression)
Fachgebiet	Statistik	Statistik
Familie	Regression model	Regression model
Entstehungsjahr	1984	2009
Urheber	Peter J. Rousseeuw	Peter J. Huber
Typ	Robust linear regression	Robust linear regression
Wegweisende Quelle	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.
Aliasnamen	LTS, least trimmed squares regression, trimmed least squares, robust regression	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler
Verwandt	5	5
Zusammenfassung	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.