Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Savvaļas bootstrap regresijas inferencē× | Permutācijas (randomizācijas) tests× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986 | 2005 |
| Autors≠ | Wu (1986); refined by Davidson & Flachaire (2008) | Good (2005); Edgington & Onghena (2007); resampling tradition |
| Tips≠ | Resampling-based regression inference | Nonparametric resampling test |
| Pirmavots≠ | Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295. DOI ↗ | Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792 |
| Citi nosaukumi | wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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 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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