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| Parametrický bootstrap× | Bootstrap inferencia× | Test permutáciou (randomizačný test)× | |
|---|---|---|---|
| Odbor | Štatistika | Štatistika | Štatistika |
| Rodina | Regression model | Regression model | Regression model |
| Rok vzniku≠ | 1993 | 1979 | 2005 |
| Tvorca≠ | Efron & Tibshirani; Davison & Hinkley | Bradley Efron | Good (2005); Edgington & Onghena (2007); resampling tradition |
| Typ≠ | Resampling-based inference (model-based) | Resampling-based inference | Nonparametric resampling test |
| Pôvodný zdroj≠ | Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. CRC Press. ISBN: 978-0412042317 | 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 |
| Ďalšie názvy≠ | parametrik bootstrap, model-based bootstrap, parametric resampling | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| Príbuzné | 5 | 5 | 5 |
| Zhrnutie≠ | 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. | 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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