विधियों की तुलना करें

	BCa बूटस्ट्रैप (पूर्वाग्रह-सुधारित और त्वरित)	रिग्रेशन अनुमान के लिए वाइल्ड बूटस्ट्रैप
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model
उद्भव वर्ष	1987	1986
प्रवर्तक	Bradley Efron	Wu (1986); refined by Davidson & Flachaire (2008)
प्रकार	Resampling confidence interval	Resampling-based regression inference
मौलिक स्रोत	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.
उपनाम	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap
संबंधित	5	5
सारांश	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.