Σύγκριση μεθόδων

	Ποσοστιαία Παλινδρόμηση (Μη Παραμετρικές Παραλλαγές)	Παλινδρόμηση Ridge
Πεδίο	Στατιστική	Μηχανική Μάθηση
Οικογένεια	Regression model	Machine learning
Έτος προέλευσης	1978	1970
Δημιουργός	Koenker & Bassett	Hoerl, A.E. & Kennard, R.W.
Τύπος	Quantile regression (nonparametric variants)	L2-regularized linear regression
Θεμελιώδης πηγή	Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Εναλλακτικές ονομασίες	quantile regression, median regression, distribution-free quantile regression, Kantil Regresyon (Nonparametric Varyantlar)	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Συναφείς	5	4
Σύνοψη	Quantile 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.