方法对比

	BCa Bootstrap（偏差校正和加速法）	贝叶斯自助法（Bayesian Bootstrap，由 Rubin 提出）	Bootstrap Inference	双重（迭代）自助法
领域	统计学	统计学	统计学	统计学
方法族	Regression model	Regression model	Regression model	Regression model
起源年份	1987	1981	1979	1986
提出者	Bradley Efron	Rubin (1981); large-sample theory by Lo (1987)	Bradley Efron	Hall (1986); Beran (1987)
类型	Resampling confidence interval	Resampling / posterior simulation	Resampling-based inference	Resampling calibration (nested bootstrap)
开创性文献	Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185.	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.
别名	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	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)
相关	5	5	5	5
摘要	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.	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.