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	Bootstrap parametrico	Bootstrap Bayesiano (Rubin)	Test di Permutazione (Randomizzazione)
Campo	Statistica	Statistica	Statistica
Famiglia	Regression model	Regression model	Regression model
Anno di origine	1993	1981	2005
Ideatore	Efron & Tibshirani; Davison & Hinkley	Rubin (1981); large-sample theory by Lo (1987)	Good (2005); Edgington & Onghena (2007); resampling tradition
Tipo	Resampling-based inference (model-based)	Resampling / posterior simulation	Nonparametric resampling test
Fonte seminale	Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. CRC Press. ISBN: 978-0412042317	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
Alias	parametrik bootstrap, model-based bootstrap, parametric resampling	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Correlati	5	5	5
Sintesi	The parametric bootstrap is a resampling method that estimates standard errors and confidence intervals by drawing repeated samples from a parametric model that has been fitted to the data. Developed in the bootstrap literature of Efron and Tibshirani (1993) and Davison and Hinkley (1997), it replaces analytic derivations for non-normal distributions and complex statistics.	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.