Bandingkan metode

	Regresi Huber	Regresi Least Trimmed Squares (LTS)	M-Estimator (Regresi Robust)	Regresi Kuadrat Terkecil Biasa (Ordinary Least Squares - OLS)
Bidang	Statistika	Statistika	Statistika	Ekonometrika
Keluarga	Regression model	Regression model	Regression model	Regression model
Tahun asal	1964	1984	2009	2019
Pencetus	Peter J. Huber	Peter J. Rousseeuw	Peter J. Huber	Wooldridge (textbook treatment); classical least squares
Tipe	Robust linear regression (M-estimation)	Robust linear regression	Robust linear regression	Linear regression
Sumber perintis	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	LTS, least trimmed squares regression, trimmed least squares, robust regression	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Terkait	5	5	5	5
Ringkasan	Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot 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.	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).