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。
| 双重（迭代）自助法× | 贝叶斯自助法（Bayesian Bootstrap，由 Rubin 提出）× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1986 | 1981 |
| 提出者≠ | Hall (1986); Beran (1987) | Rubin (1981); large-sample theory by Lo (1987) |
| 类型≠ | Resampling calibration (nested bootstrap) | Resampling / posterior simulation |
| 开创性文献≠ | Hall, 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 ↗ |
| 别名≠ | iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap) | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap |
| 相关 | 5 | 5 |
| 摘要≠ | 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 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. |
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