方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 贝叶斯自助法（Bayesian Bootstrap，由 Rubin 提出）× | 块自举（移动块和固定块）× | 置换 (随机化) 检验× | |
|---|---|---|---|
| 领域 | 统计学 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model | Regression model |
| 起源年份≠ | 1981 | 1989 | 2005 |
| 提出者≠ | Rubin (1981); large-sample theory by Lo (1987) | Künsch (moving block, 1989); Politis & Romano (stationary, 1994) | Good (2005); Edgington & Onghena (2007); resampling tradition |
| 类型≠ | Resampling / posterior simulation | Resampling inference for dependent data | Nonparametric resampling test |
| 开创性文献≠ | 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 ↗ | Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792 |
| 别名≠ | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary) | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| 相关 | 5 | 5 | 5 |
| 摘要≠ | 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). | 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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