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| Bayesiansk bootstrap (Rubin)× | Block Bootstrap (Moving Block och Stationary)× | Bootstrapinferens× | |
|---|---|---|---|
| Ämnesområde | Statistik | Statistik | Statistik |
| Familj | Regression model | Regression model | Regression model |
| Ursprungsår≠ | 1981 | 1989 | 1979 |
| Upphovsperson≠ | Rubin (1981); large-sample theory by Lo (1987) | Künsch (moving block, 1989); Politis & Romano (stationary, 1994) | Bradley Efron |
| Typ≠ | Resampling / posterior simulation | Resampling inference for dependent data | Resampling-based inference |
| Ursprungskälla≠ | Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. 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 ↗ |
| Alias≠ | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary) | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı |
| Närliggande | 5 | 5 | 5 |
| Sammanfattning≠ | 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. | 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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