Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Parametriskais bootstrap× | Savvaļas bootstrap regresijas inferencē× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1993 | 1986 |
| Autors≠ | Efron & Tibshirani; Davison & Hinkley | Wu (1986); refined by Davidson & Flachaire (2008) |
| Tips≠ | Resampling-based inference (model-based) | Resampling-based regression inference |
| Pirmavots≠ | Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. CRC Press. ISBN: 978-0412042317 | Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295. DOI ↗ |
| Citi nosaukumi≠ | parametrik bootstrap, model-based bootstrap, parametric resampling | wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The parametric bootstrap is a resampling method that estimates standard errors and confidence intervals by drawing repeated samples from a parametric model that has been fitted to the data. Developed in the bootstrap literature of Efron and Tibshirani (1993) and Davison and Hinkley (1997), it replaces analytic derivations for non-normal distributions and complex statistics. | 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. |
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