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	BCa बूटस्ट्रैप (पूर्वाग्रह-सुधारित और त्वरित)	बेयसियन बूटस्ट्रैप (रूबिन)	बूटस्ट्रैप अनुमान	डबल (इटरेटेड) बूटस्ट्रैप
क्षेत्र	सांख्यिकी	सांख्यिकी	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model	Regression model	Regression model
उद्भव वर्ष	1987	1981	1979	1986
प्रवर्तक	Bradley Efron	Rubin (1981); large-sample theory by Lo (1987)	Bradley Efron	Hall (1986); Beran (1987)
प्रकार	Resampling confidence interval	Resampling / posterior simulation	Resampling-based inference	Resampling calibration (nested bootstrap)
मौलिक स्रोत	Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185.	Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452.
उपनाम	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap)
संबंधित	5	5	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 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.	Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples.	The double bootstrap is a resampling method that calibrates a bootstrap confidence interval with a second, nested layer of bootstrap to bring its actual coverage closer to the nominal level. Introduced by Hall (1986) and Beran (1987), it is especially valuable for small samples and skewed distributions where a single-layer bootstrap under-covers.