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| Bayesian Bootstrap (Rubin)× | 블록 부트스트랩 (이동 블록 및 정상성)× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1981 | 1989 |
| 창시자≠ | Rubin (1981); large-sample theory by Lo (1987) | Künsch (moving block, 1989); Politis & Romano (stationary, 1994) |
| 유형≠ | Resampling / posterior simulation | Resampling inference for dependent data |
| 원전≠ | 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 ↗ |
| 별칭 | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary) |
| 관련 | 5 | 5 |
| 요약≠ | 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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