विधियों की तुलना करें

	बेयसियन बूटस्ट्रैप (रूबिन)	BCa बूटस्ट्रैप (पूर्वाग्रह-सुधारित और त्वरित)
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model
उद्भव वर्ष	1981	1987
प्रवर्तक	Rubin (1981); large-sample theory by Lo (1987)	Bradley Efron
प्रकार	Resampling / posterior simulation	Resampling confidence interval
मौलिक स्रोत	Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.	Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185.
उपनाम	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval
संबंधित	5	5
सारांश	The Bayesian Bootstrap, introduced by Donald B. Rubin in 1981, is a resampling method that produces a Bayesian counterpart to the frequentist bootstrap by assigning each observation a random weight drawn from a Dirichlet distribution. It yields a full posterior distribution for a statistic and allows prior information to be incorporated.	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.