Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Дикий бутстреп для регресійних висновків× | Блоковий бутстреп (рухомий блок та стаціонарний)× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1986 | 1989 |
| Автор методу≠ | Wu (1986); refined by Davidson & Flachaire (2008) | Künsch (moving block, 1989); Politis & Romano (stationary, 1994) |
| Тип≠ | Resampling-based regression inference | Resampling inference for dependent data |
| Основоположне джерело≠ | Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295. DOI ↗ | Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241. DOI ↗ |
| Інші назви≠ | wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap | moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary) |
| 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. | Block bootstrap is a resampling method for dependent, autocorrelated time-series data: instead of resampling single observations, it resamples whole blocks of consecutive observations so the serial-correlation structure is preserved. The moving block variant was introduced by Künsch (1989) and the stationary variant by Politis and Romano (1994). |
| ScholarGateНабір даних ↗ |
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