Comparer des méthodes

	Erreurs-types robustes aux clusters	Bootstrap sauvage pour l'inférence de régression
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1986	1986
Auteur d'origine	Liang & Zeger (GEE sandwich); Cameron & Miller (practitioner synthesis)	Wu (1986); refined by Davidson & Flachaire (2008)
Type	Robust variance estimation for regression	Resampling-based regression inference
Source fondatrice	Liang, K. Y. & Zeger, S. L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73(1), 13-22.	Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295.
Alias	clustered standard errors, cluster-robust inference, clustered variance estimator, Küme Robust Standart Hatalar	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap
Apparentées	4	5
Résumé	Cluster-robust standard errors correct the variance of regression coefficients when observations are correlated within clusters such as schools, hospitals, or regions. The clustered sandwich estimator grew out of Liang & Zeger's (1986) generalized estimating equations and was synthesized for applied work by Cameron & Miller (2015), delivering valid inference when ordinary standard errors would be too small.	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.