Compară metode

	Bootstrap BCa (Corectat pentru Bias și Accelerat)	Bootstrap sălbatic pentru inferență în regresie
Domeniu	Statistică	Statistică
Familie	Regression model	Regression model
Anul apariției	1987	1986
Autorul original	Bradley Efron	Wu (1986); refined by Davidson & Flachaire (2008)
Tip	Resampling confidence interval	Resampling-based regression inference
Sursa seminală	Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185.	Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295.
Denumiri alternative	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap
Înrudite	5	5
Rezumat	The BCa bootstrap is a resampling method, introduced by Bradley Efron in 1987, that produces more accurate confidence intervals than the plain percentile bootstrap by applying a bias correction and an acceleration adjustment. It is recommended for skewed distributions and small samples.	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.