Salīdzināt metodes

	Beijeski Bootstrap (Rubin)	BCa Būtapstraps (koriģēts pret novirzi un paātrināts)	Bootstrap Inference	Permutācijas (randomizācijas) tests
Nozare	Statistika	Statistika	Statistika	Statistika
Saime	Regression model	Regression model	Regression model	Regression model
Izcelsmes gads	1981	1987	1979	2005
Autors	Rubin (1981); large-sample theory by Lo (1987)	Bradley Efron	Bradley Efron	Good (2005); Edgington & Onghena (2007); resampling tradition
Tips	Resampling / posterior simulation	Resampling confidence interval	Resampling-based inference	Nonparametric resampling test
Pirmavots	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.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Citi nosaukumi	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Saistītās	5	5	5	5
Kopsavilkums	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.	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 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.