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Huberova regresija×Kvantilna regresija×
PodručjeStatistikaEkonometrija
ObiteljRegression modelRegression model
Godina nastanka19641978
TvoracPeter J. HuberKoenker & Bassett
VrstaRobust linear regression (M-estimation)Conditional quantile regression
Temeljni izvorHuber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101. DOI ↗Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗
Drugi naziviHuber M-estimator, Huber loss regression, robust regression, Huber Regresyonuconditional quantile regression, regression quantiles, Kantil Regresyon
Srodne55
SažetakHuber 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.Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.
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ScholarGateUsporedite metode: Huber Regression · Quantile Regression. Preuzeto 2026-06-17 s https://scholargate.app/hr/compare