Comparer des méthodes

	Bootstrap paramétrique	Le Bootstrap Bayésien (Rubin)	Test par permutation (ou randomisation)
Domaine	Statistique	Statistique	Statistique
Famille	Regression model	Regression model	Regression model
Année d'origine	1993	1981	2005
Auteur d'origine	Efron & Tibshirani; Davison & Hinkley	Rubin (1981); large-sample theory by Lo (1987)	Good (2005); Edgington & Onghena (2007); resampling tradition
Type	Resampling-based inference (model-based)	Resampling / posterior simulation	Nonparametric resampling test
Source fondatrice	Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. CRC Press. ISBN: 978-0412042317	Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Alias	parametrik bootstrap, model-based bootstrap, parametric resampling	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Apparentées	5	5	5
Résumé	The parametric bootstrap is a resampling method that estimates standard errors and confidence intervals by drawing repeated samples from a parametric model that has been fitted to the data. Developed in the bootstrap literature of Efron and Tibshirani (1993) and Davison and Hinkley (1997), it replaces analytic derivations for non-normal distributions and complex statistics.	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 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.