Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Блоковий бутстреп (рухомий блок та стаціонарний)× | Метод ковзного виключення (Jackknife Resampling)× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1989 | 1956 |
| Автор методу≠ | Künsch (moving block, 1989); Politis & Romano (stationary, 1994) | Quenouille (1956); reviewed by Miller (1974) |
| Тип≠ | Resampling inference for dependent data | Resampling / bias and variance estimation |
| Основоположне джерело≠ | Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241. DOI ↗ | Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360. DOI ↗ |
| Інші назви≠ | moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary) | leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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). | The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability. |
| ScholarGateНабір даних ↗ |
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