Compară metode

	Bootstrap bayesian (Rubin)	Bootstrap BCa (Corectat pentru Bias și Accelerat)	Inferența Bootstrap
Domeniu	Statistică	Statistică	Statistică
Familie	Regression model	Regression model	Regression model
Anul apariției	1981	1987	1979
Autorul original	Rubin (1981); large-sample theory by Lo (1987)	Bradley Efron	Bradley Efron
Tip	Resampling / posterior simulation	Resampling confidence interval	Resampling-based inference
Sursa seminală	Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.	Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.
Denumiri alternative	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı
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.	The BCa bootstrap is a resampling method, introduced by Bradley Efron in 1987, that produces more accurate confidence intervals than the plain percentile bootstrap by applying a bias correction and an acceleration adjustment. It is recommended for skewed distributions and small samples.	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.