Comparer des méthodes

	Rééchantillonnage par jackknife	Régression quantile (variantes non paramétriques)
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1956	1978
Auteur d'origine	Quenouille (1956); reviewed by Miller (1974)	Koenker & Bassett
Type	Resampling / bias and variance estimation	Quantile regression (nonparametric variants)
Source fondatrice	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.	Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme	quantile regression, median regression, distribution-free quantile regression, Kantil Regresyon (Nonparametric Varyantlar)
Apparentées	5	5
Résumé	The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability.	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.