Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Дикий бутстреп для регресійних висновків× | Байєсівський бутстреп (Rubin)× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1986 | 1981 |
| Автор методу≠ | Wu (1986); refined by Davidson & Flachaire (2008) | Rubin (1981); large-sample theory by Lo (1987) |
| Тип≠ | Resampling-based regression inference | Resampling / posterior simulation |
| Основоположне джерело≠ | Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295. DOI ↗ | Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI ↗ |
| Інші назви≠ | wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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. | 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. |
| ScholarGateНабір даних ↗ |
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