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| Bayesian Bootstrap (Rubin)× | 순열 (무작위화) 검정× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1981 | 2005 |
| 창시자≠ | Rubin (1981); large-sample theory by Lo (1987) | Good (2005); Edgington & Onghena (2007); resampling tradition |
| 유형≠ | Resampling / posterior simulation | Nonparametric resampling test |
| 원전≠ | Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI ↗ | Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792 |
| 별칭≠ | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| 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. | The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value. |
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