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| Bootstrap BCa (skorygowany ze względu na obciążenie i przyspieszony)× | Estymacja bootstrapowa× | Test permutacyjny (randomizacyjny)× | |
|---|---|---|---|
| Dziedzina | Statystyka | Statystyka | Statystyka |
| Rodzina | Regression model | Regression model | Regression model |
| Rok powstania≠ | 1987 | 1979 | 2005 |
| Twórca≠ | Bradley Efron | Bradley Efron | Good (2005); Edgington & Onghena (2007); resampling tradition |
| Typ≠ | Resampling confidence interval | Resampling-based inference | Nonparametric resampling test |
| Źródło pierwotne≠ | Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185. 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 |
| Inne nazwy≠ | BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| Pokrewne | 5 | 5 | 5 |
| Podsumowanie≠ | The BCa bootstrap is a resampling method, introduced by Bradley Efron in 1987, that produces more accurate confidence intervals than the plain percentile bootstrap by applying a bias correction and an acceleration adjustment. It is recommended for skewed distributions and small samples. | 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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