Σύγκριση μεθόδων

	Μπεϋζιανή Μπουτστραπ (Rubin)	Block Bootstrap (Moving Block και Stationary)	Επαγωγή Bootstrap
Πεδίο	Στατιστική	Στατιστική	Στατιστική
Οικογένεια	Regression model	Regression model	Regression model
Έτος προέλευσης	1981	1989	1979
Δημιουργός	Rubin (1981); large-sample theory by Lo (1987)	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Bradley Efron
Τύπος	Resampling / posterior simulation	Resampling inference for dependent data	Resampling-based inference
Θεμελιώδης πηγή	Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.	Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.
Εναλλακτικές ονομασίες	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı
Συναφείς	5	5	5
Σύνοψη	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.	Block bootstrap is a resampling method for dependent, autocorrelated time-series data: instead of resampling single observations, it resamples whole blocks of consecutive observations so the serial-correlation structure is preserved. The moving block variant was introduced by Künsch (1989) and the stationary variant by Politis and Romano (1994).	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.