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회귀 추론을 위한 와일드 부트스트랩×Bayesian Bootstrap (Rubin)×블록 부트스트랩 (이동 블록 및 정상성)×
분야통계학통계학통계학
계열Regression modelRegression modelRegression model
기원 연도198619811989
창시자Wu (1986); refined by Davidson & Flachaire (2008)Rubin (1981); large-sample theory by Lo (1987)Künsch (moving block, 1989); Politis & Romano (stationary, 1994)
유형Resampling-based regression inferenceResampling / posterior simulationResampling inference for dependent data
원전Wu, 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 ↗
별칭wild 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)
관련555
요약The 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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ScholarGate방법 비교: Wild Bootstrap · Bayesian Bootstrap · Block Bootstrap. 2026-06-16에 다음에서 검색함: https://scholargate.app/ko/compare