Methoden vergleichen
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| Wild Bootstrap für Regressionsinferenz× | Block-Bootstrap (Moving Block und Stationär)× | Bootstrap-Inferenz× | |
|---|---|---|---|
| Fachgebiet | Statistik | Statistik | Statistik |
| Familie | Regression model | Regression model | Regression model |
| Entstehungsjahr≠ | 1986 | 1989 | 1979 |
| Urheber≠ | Wu (1986); refined by Davidson & Flachaire (2008) | Künsch (moving block, 1989); Politis & Romano (stationary, 1994) | Bradley Efron |
| Typ≠ | Resampling-based regression inference | Resampling inference for dependent data | Resampling-based inference |
| Wegweisende Quelle≠ | 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 ↗ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ |
| Aliasnamen≠ | wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap | moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary) | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı |
| Verwandt | 5 | 5 | 5 |
| Zusammenfassung≠ | 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). | 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. |
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