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）× | 双重（迭代）自助法× | 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 | 1986 | 2005 |
| 提出者≠ | Rubin (1981); large-sample theory by Lo (1987) | Hall (1986); Beran (1987) | Good (2005); Edgington & Onghena (2007); resampling tradition |
| 类型≠ | Resampling / posterior simulation | Resampling calibration (nested bootstrap) | Nonparametric resampling test |
| 开创性文献≠ | Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI ↗ | Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452. 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 | iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap) | 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. | The 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 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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