Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Bootstrap Inference× | Dublā (iterētā) bootstrap metode× | Permutācijas (randomizācijas) tests× | |
|---|---|---|---|
| Nozare | Statistika | Statistika | Statistika |
| Saime | Regression model | Regression model | Regression model |
| Izcelsmes gads≠ | 1979 | 1986 | 2005 |
| Autors≠ | Bradley Efron | Hall (1986); Beran (1987) | Good (2005); Edgington & Onghena (2007); resampling tradition |
| Tips≠ | Resampling-based inference | Resampling calibration (nested bootstrap) | Nonparametric resampling test |
| Pirmavots≠ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ | Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452. DOI ↗ | Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792 |
| Citi nosaukumi | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap) | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| Saistītās | 5 | 5 | 5 |
| Kopsavilkums≠ | 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 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. | 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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