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Wild Bootstrap for Regression Inference×贝叶斯自助法(Bayesian Bootstrap,由 Rubin 提出)×块自举(移动块和固定块)×
领域统计学统计学统计学
方法族Regression modelRegression modelRegression model
起源年份198619811989
提出者Wu (1986); refined by Davidson & Flachaire (2008)Rubin (1981); large-sample theory by Lo (1987)Künsch (moving block, 1989); Politis & Romano (stationary, 1994)
类型Resampling-based regression inferenceResampling / posterior simulationResampling 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. DOI ↗Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI ↗Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241. DOI ↗
别名wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild BootstrapBayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrapmoving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)
相关555
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.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.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).
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ScholarGate方法对比: Wild Bootstrap · Bayesian Bootstrap · Block Bootstrap. 于 2026-06-17 检索自 https://scholargate.app/zh/compare