Sammenlign metoder

	Huber-regresjon	M-estimatorer (Robust regresjon)
Fagfelt	Statistikk	Statistikk
Familie	Regression model	Regression model
Opprinnelsesår	1964	2009
Opphavsperson	Peter J. Huber	Peter J. Huber
Type	Robust linear regression (M-estimation)	Robust linear regression
Opprinnelig kilde	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.	Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.
Alias	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler
Relaterte	5	5
Sammendrag	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.	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.