Σύγκριση μεθόδων

	Ενισχυτική Μέθοδος BCa (Διορθωμένη ως προς την Μεροληψία και Επιταχυνόμενη)	Μπεϋζιανή Μπουτστραπ (Rubin)	Διπλός (Επαναληπτικός) Bootstrap
Πεδίο	Στατιστική	Στατιστική	Στατιστική
Οικογένεια	Regression model	Regression model	Regression model
Έτος προέλευσης	1987	1981	1986
Δημιουργός	Bradley Efron	Rubin (1981); large-sample theory by Lo (1987)	Hall (1986); Beran (1987)
Τύπος	Resampling confidence interval	Resampling / posterior simulation	Resampling calibration (nested bootstrap)
Θεμελιώδης πηγή	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.	Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452.
Εναλλακτικές ονομασίες	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap)
Συναφείς	5	5	5
Σύνοψη	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.	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.