方法对比

	Wild Bootstrap for Regression Inference	块自举（移动块和固定块）
领域	统计学	统计学
方法族	Regression model	Regression model
起源年份	1986	1989
提出者	Wu (1986); refined by Davidson & Flachaire (2008)	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)
类型	Resampling-based regression inference	Resampling inference for dependent data
开创性文献	Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295.	Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241.
别名	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap	moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)
相关	5	5
摘要	The wild bootstrap is a resampling method for regression models with heteroscedastic errors, introduced by Wu (1986) and refined by Davidson and Flachaire (2008). It builds a bootstrap distribution by rescaling each fitted residual with a random sign, so that standard errors and confidence intervals stay valid when the error variance is not constant or the data are clustered.	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).