Comparar métodos

	Bootstrap Bayesiano (Rubin)	Inferência Bootstrap	Bootstrap Duplo (Iterado)
Área	Estatística	Estatística	Estatística
Família	Regression model	Regression model	Regression model
Ano de origem	1981	1979	1986
Autor original	Rubin (1981); large-sample theory by Lo (1987)	Bradley Efron	Hall (1986); Beran (1987)
Tipo	Resampling / posterior simulation	Resampling-based inference	Resampling calibration (nested bootstrap)
Fonte seminal	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.	Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452.
Outros nomes	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap)
Relacionados	5	5	5
Resumo	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.	The double bootstrap is a resampling method that calibrates a bootstrap confidence interval with a second, nested layer of bootstrap to bring its actual coverage closer to the nominal level. Introduced by Hall (1986) and Beran (1987), it is especially valuable for small samples and skewed distributions where a single-layer bootstrap under-covers.