Comparer des méthodes

	Bootstrap sauvage pour l'inférence de régression	Bootstrap par blocs (blocs mobiles et stationnaires)
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1986	1989
Auteur d'origine	Wu (1986); refined by Davidson & Flachaire (2008)	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)
Type	Resampling-based regression inference	Resampling inference for dependent data
Source fondatrice	Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295.	Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241.
Alias	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap	moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)
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.	Block bootstrap is a resampling method for dependent, autocorrelated time-series data: instead of resampling single observations, it resamples whole blocks of consecutive observations so the serial-correlation structure is preserved. The moving block variant was introduced by Künsch (1989) and the stationary variant by Politis and Romano (1994).