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| Bootstrap Doppio (Iterato)× | Inferenza Bootstrap× | Test di Permutazione (Randomizzazione)× | |
|---|---|---|---|
| Campo | Statistica | Statistica | Statistica |
| Famiglia | Regression model | Regression model | Regression model |
| Anno di origine≠ | 1986 | 1979 | 2005 |
| Ideatore≠ | Hall (1986); Beran (1987) | Bradley Efron | Good (2005); Edgington & Onghena (2007); resampling tradition |
| Tipo≠ | Resampling calibration (nested bootstrap) | Resampling-based inference | Nonparametric resampling test |
| Fonte seminale≠ | Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452. 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 |
| Alias | iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap) | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| Correlati | 5 | 5 | 5 |
| Sintesi≠ | The double bootstrap is a resampling method that calibrates a bootstrap confidence interval with a second, nested layer of bootstrap to bring its actual coverage closer to the nominal level. Introduced by Hall (1986) and Beran (1987), it is especially valuable for small samples and skewed distributions where a single-layer bootstrap under-covers. | 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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