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	Bootstrap BCa (skorygowany ze względu na obciążenie i przyspieszony)	Bayesowski Bootstrap (Rubin)	Test permutacyjny (randomizacyjny)
Dziedzina	Statystyka	Statystyka	Statystyka
Rodzina	Regression model	Regression model	Regression model
Rok powstania	1987	1981	2005
Twórca	Bradley Efron	Rubin (1981); large-sample theory by Lo (1987)	Good (2005); Edgington & Onghena (2007); resampling tradition
Typ	Resampling confidence interval	Resampling / posterior simulation	Nonparametric resampling test
Źródło pierwotne	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.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Inne nazwy	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Pokrewne	5	5	5
Podsumowanie	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 permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value.