Comparer des méthodes

	Régression quantile (variantes non paramétriques)	Estimation par noyau de la densité et tests de distribution (KDE)
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1978	1956
Auteur d'origine	Koenker & Bassett	Rosenblatt (1956); Parzen (1962); textbook treatment by Silverman
Type	Quantile regression (nonparametric variants)	Nonparametric density estimation
Source fondatrice	Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837.
Alias	quantile regression, median regression, distribution-free quantile regression, Kantil Regresyon (Nonparametric Varyantlar)	kernel density estimate, KDE, Parzen window estimation, nonparametric density estimation
Apparentées	5	4
Résumé	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.	Kernel Density Estimation is a nonparametric method that estimates a continuous probability density by placing a smooth kernel function over each observation, without assuming any parametric distribution. It traces back to Rosenblatt (1956) and the textbook treatment by Silverman (1986), and it also supports distribution-comparison tests built on the estimated densities.