方法对比

	双重（迭代）自助法	块自举（移动块和固定块）	Bootstrap Inference
领域	统计学	统计学	统计学
方法族	Regression model	Regression model	Regression model
起源年份	1986	1989	1979
提出者	Hall (1986); Beran (1987)	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Bradley Efron
类型	Resampling calibration (nested bootstrap)	Resampling inference for dependent data	Resampling-based inference
开创性文献	Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452.	Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.
别名	iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap)	moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı
相关	5	5	5
摘要	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.	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).	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.