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이중 (반복) 부트스트랩×Bayesian Bootstrap (Rubin)×블록 부트스트랩 (이동 블록 및 정상성)×
분야통계학통계학통계학
계열Regression modelRegression modelRegression model
기원 연도198619811989
창시자Hall (1986); Beran (1987)Rubin (1981); large-sample theory by Lo (1987)Künsch (moving block, 1989); Politis & Romano (stationary, 1994)
유형Resampling calibration (nested bootstrap)Resampling / posterior simulationResampling inference for dependent data
원전Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452. 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 ↗
별칭iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap)Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrapmoving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)
관련555
요약The 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.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: Double Bootstrap · Bayesian Bootstrap · Block Bootstrap. 2026-06-15에 다음에서 검색함: https://scholargate.app/ko/compare