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Kvantilregresjon (ikke-parametriske varianter)×Ridge Regression×
FagfeltStatistikkMaskinlæring
FamilieRegression modelMachine learning
Opprinnelsesår19781970
OpphavspersonKoenker & BassettHoerl, A.E. & Kennard, R.W.
TypeQuantile regression (nonparametric variants)L2-regularized linear regression
Opprinnelig kildeKoenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗
Aliasquantile regression, median regression, distribution-free quantile regression, Kantil Regresyon (Nonparametric Varyantlar)Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Relaterte54
SammendragQuantile regression, introduced by Koenker and Bassett in 1978, models a chosen conditional quantile (such as the median or the 25th and 75th percentiles) of a continuous outcome rather than its mean. Its nonparametric variants fit these quantile relationships without assuming a distribution for the errors, making them a robust complement to mean-based regression on skewed data.Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.
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ScholarGateSammenlign metoder: Nonparametric Quantile Regression · Ridge Regression. Hentet 2026-06-18 fra https://scholargate.app/no/compare