Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Подвійний (ітераційний) бутстреп× | Дикий бутстреп для регресійних висновків× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Regression model | Regression model |
| Рік появи | 1986 | 1986 |
| Автор методу≠ | Hall (1986); Beran (1987) | Wu (1986); refined by Davidson & Flachaire (2008) |
| Тип≠ | Resampling calibration (nested bootstrap) | Resampling-based regression inference |
| Основоположне джерело≠ | Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452. DOI ↗ | Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295. DOI ↗ |
| Інші назви | iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap) | wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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 wild bootstrap is a resampling method for regression models with heteroscedastic errors, introduced by Wu (1986) and refined by Davidson and Flachaire (2008). It builds a bootstrap distribution by rescaling each fitted residual with a random sign, so that standard errors and confidence intervals stay valid when the error variance is not constant or the data are clustered. |
| ScholarGateНабір даних ↗ |
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