Sammenlign metoder

	BCa Bootstrap (Bias-korrigert og akselerert)	Wild Bootstrap for Regression Inference
Fagfelt	Statistikk	Statistikk
Familie	Regression model	Regression model
Opprinnelsesår	1987	1986
Opphavsperson	Bradley Efron	Wu (1986); refined by Davidson & Flachaire (2008)
Type	Resampling confidence interval	Resampling-based regression inference
Opprinnelig kilde	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.
Alias	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap
Relaterte	5	5
Sammendrag	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.