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| Wild Bootstrap til Regressionsinferens× | Bayesian Bootstrap (Rubin)× | Bootstrap-inferens× | |
|---|---|---|---|
| Fagområde | Statistik | Statistik | Statistik |
| Familie | Regression model | Regression model | Regression model |
| Oprindelsesår≠ | 1986 | 1981 | 1979 |
| Ophavsperson≠ | Wu (1986); refined by Davidson & Flachaire (2008) | Rubin (1981); large-sample theory by Lo (1987) | Bradley Efron |
| Type≠ | Resampling-based regression inference | Resampling / posterior simulation | Resampling-based inference |
| Oprindelig kilde≠ | 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 ↗ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ |
| Aliasser≠ | wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı |
| Relaterede | 5 | 5 | 5 |
| Resumé≠ | 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. | 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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