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Робусна линеарна регресија×Kvantilna regresija×
OblastMašinsko učenjeEkonometrija
PorodicaMachine learningRegression model
Godina nastanka1964–19871978
TvoracHuber, P. J.; Rousseeuw, P. J.Koenker & Bassett
TipOutlier-resistant supervised regressionConditional 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 nazivirobust regression, M-estimator regression, Huber regression, outlier-resistant regressionconditional quantile regression, regression quantiles, Kantil Regresyon
Srodne55
SažetakRobust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation.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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ScholarGateUporedite metode: Robust Linear Regression · Quantile Regression. Preuzeto 2026-06-15 sa https://scholargate.app/sr/compare