Comparer des méthodes

	Bootstrap sauvage pour l'inférence de régression	Test par permutation (ou randomisation)
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1986	2005
Auteur d'origine	Wu (1986); refined by Davidson & Flachaire (2008)	Good (2005); Edgington & Onghena (2007); resampling tradition
Type	Resampling-based regression inference	Nonparametric resampling test
Source fondatrice	Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Alias	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Apparentées	5	5
Résumé	The wild bootstrap is a resampling method for regression models with heteroscedastic errors, introduced by Wu (1986) and refined by Davidson and Flachaire (2008). It builds a bootstrap distribution by rescaling each fitted residual with a random sign, so that standard errors and confidence intervals stay valid when the error variance is not constant or the data are clustered.	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.