Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Блочная бутстрэп-выборка (скользящий блок и стационарная)× | Бутстреп-вывод× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1989 | 1979 |
| Автор метода≠ | Künsch (moving block, 1989); Politis & Romano (stationary, 1994) | Bradley Efron |
| Тип≠ | Resampling inference for dependent data | Resampling-based inference |
| Основополагающий источник≠ | Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241. DOI ↗ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ |
| Другие названия≠ | moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary) | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı |
| Связанные | 5 | 5 |
| Сводка≠ | 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). | Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples. |
| ScholarGateНабор данных ↗ |
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