Comparar métodos

	Bootstrap BCa (Correção de Viés e Aceleração)	Inferência Bootstrap	Bootstrap Duplo (Iterado)
Área	Estatística	Estatística	Estatística
Família	Regression model	Regression model	Regression model
Ano de origem	1987	1979	1986
Autor original	Bradley Efron	Bradley Efron	Hall (1986); Beran (1987)
Tipo	Resampling confidence interval	Resampling-based inference	Resampling calibration (nested bootstrap)
Fonte seminal	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.	Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452.
Outros nomes	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap)
Relacionados	5	5	5
Resumo	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.	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.