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	Parametryczny bootstrap	Bayesowski Bootstrap (Rubin)	Estymacja bootstrapowa
Dziedzina	Statystyka	Statystyka	Statystyka
Rodzina	Regression model	Regression model	Regression model
Rok powstania	1993	1981	1979
Twórca	Efron & Tibshirani; Davison & Hinkley	Rubin (1981); large-sample theory by Lo (1987)	Bradley Efron
Typ	Resampling-based inference (model-based)	Resampling / posterior simulation	Resampling-based inference
Źródło pierwotne	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.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.
Inne nazwy	parametrik bootstrap, model-based bootstrap, parametric resampling	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı
Pokrewne	5	5	5
Podsumowanie	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.	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.