Compară metode

	Bootstrap bayesian (Rubin)	Bootstrap pe blocuri (blocuri mobile și staționare)	Reeșantionarea Jackknife
Domeniu	Statistică	Statistică	Statistică
Familie	Regression model	Regression model	Regression model
Anul apariției	1981	1989	1956
Autorul original	Rubin (1981); large-sample theory by Lo (1987)	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Quenouille (1956); reviewed by Miller (1974)
Tip	Resampling / posterior simulation	Resampling inference for dependent data	Resampling / bias and variance estimation
Sursa seminală	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.	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.
Denumiri alternative	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme
Înrudite	5	5	5
Rezumat	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).	The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability.