Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Двойной (итерированный) бутстрэп× | Байесовский бутстрэп (Рубин)× | Тест перестановок (рандомизация)× | |
|---|---|---|---|
| Область | Статистика | Статистика | Статистика |
| Семейство | Regression model | Regression model | Regression model |
| Год появления≠ | 1986 | 1981 | 2005 |
| Автор метода≠ | Hall (1986); Beran (1987) | Rubin (1981); large-sample theory by Lo (1987) | Good (2005); Edgington & Onghena (2007); resampling tradition |
| Тип≠ | Resampling calibration (nested bootstrap) | Resampling / posterior simulation | Nonparametric resampling test |
| Основополагающий источник≠ | 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 ↗ | Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792 |
| Другие названия≠ | iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap) | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| Связанные | 5 | 5 | 5 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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