Comparer des méthodes

	Bootstrap sauvage pour l'inférence de régression	Le Bootstrap Bayésien (Rubin)	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Statistique	Statistique	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	1986	1981	2019
Auteur d'origine	Wu (1986); refined by Davidson & Flachaire (2008)	Rubin (1981); large-sample theory by Lo (1987)	Wooldridge (textbook treatment); classical least squares
Type	Resampling-based regression inference	Resampling / posterior simulation	Linear regression
Source fondatrice	Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295.	Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	5	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 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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).