Bandingkan metode

	M-Estimator (Regresi Robust)	Regresi Least Trimmed Squares (LTS)
Bidang	Statistika	Statistika
Keluarga	Regression model	Regression model
Tahun asal	2009	1984
Pencetus	Peter J. Huber	Peter J. Rousseeuw
Tipe	Robust linear regression	Robust linear regression
Sumber perintis	Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.
Alias	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler	LTS, least trimmed squares regression, trimmed least squares, robust regression
Terkait	5	5
Ringkasan	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.	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.