विधियों की तुलना करें

	बेयसियन बूटस्ट्रैप (रूबिन)	डबल (इटरेटेड) बूटस्ट्रैप	क्रमचय (यादृच्छिकीकरण) परीक्षण
क्षेत्र	सांख्यिकी	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model	Regression model
उद्भव वर्ष	1981	1986	2005
प्रवर्तक	Rubin (1981); large-sample theory by Lo (1987)	Hall (1986); Beran (1987)	Good (2005); Edgington & Onghena (2007); resampling tradition
प्रकार	Resampling / posterior simulation	Resampling calibration (nested bootstrap)	Nonparametric resampling test
मौलिक स्रोत	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.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
उपनाम	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap)	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
संबंधित	5	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 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.	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.