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| A Bayesian Bootstrap (Rubin)× | Bootstrap-becslés× | Permutációs (randomizációs) teszt× | |
|---|---|---|---|
| Tudományterület | Statisztika | Statisztika | Statisztika |
| Módszercsalád | Regression model | Regression model | Regression model |
| Keletkezés éve≠ | 1981 | 1979 | 2005 |
| Megalkotó≠ | Rubin (1981); large-sample theory by Lo (1987) | Bradley Efron | Good (2005); Edgington & Onghena (2007); resampling tradition |
| Típus≠ | Resampling / posterior simulation | Resampling-based inference | Nonparametric resampling test |
| Alapmű≠ | 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 ↗ | Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792 |
| Alternatív nevek≠ | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| Kapcsolódó | 5 | 5 | 5 |
| Összefoglaló≠ | 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. | The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value. |
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