Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Параметричний бутстреп× | Байєсівський бутстреп (Rubin)× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1993 | 1981 |
| Автор методу≠ | Efron & Tibshirani; Davison & Hinkley | Rubin (1981); large-sample theory by Lo (1987) |
| Тип≠ | Resampling-based inference (model-based) | Resampling / posterior simulation |
| Основоположне джерело≠ | Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. CRC Press. ISBN: 978-0412042317 | Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI ↗ |
| Інші назви | parametrik bootstrap, model-based bootstrap, parametric resampling | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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 Bayesian Bootstrap, introduced by Donald B. Rubin in 1981, is a resampling method that produces a Bayesian counterpart to the frequentist bootstrap by assigning each observation a random weight drawn from a Dirichlet distribution. It yields a full posterior distribution for a statistic and allows prior information to be incorporated. |
| ScholarGateНабір даних ↗ |
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