Comparer des méthodes

	Le Bootstrap Bayésien (Rubin)	Rééchantillonnage par jackknife	Test par permutation (ou randomisation)
Domaine	Statistique	Statistique	Statistique
Famille	Regression model	Regression model	Regression model
Année d'origine	1981	1956	2005
Auteur d'origine	Rubin (1981); large-sample theory by Lo (1987)	Quenouille (1956); reviewed by Miller (1974)	Good (2005); Edgington & Onghena (2007); resampling tradition
Type	Resampling / posterior simulation	Resampling / bias and variance estimation	Nonparametric resampling test
Source fondatrice	Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Alias	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Apparentées	5	5	5
Résumé	The Bayesian Bootstrap, introduced by Donald B. Rubin in 1981, is a resampling method that produces a Bayesian counterpart to the frequentist bootstrap by assigning each observation a random weight drawn from a Dirichlet distribution. It yields a full posterior distribution for a statistic and allows prior information to be incorporated.	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.	The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value.