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| Bootstrap parametrico× | Bootstrap Bayesiano (Rubin)× | Bootstrap BCa (corretto per distorsione e accelerazione)× | |
|---|---|---|---|
| Campo | Statistica | Statistica | Statistica |
| Famiglia | Regression model | Regression model | Regression model |
| Anno di origine≠ | 1993 | 1981 | 1987 |
| Ideatore≠ | Efron & Tibshirani; Davison & Hinkley | Rubin (1981); large-sample theory by Lo (1987) | Bradley Efron |
| Tipo≠ | Resampling-based inference (model-based) | Resampling / posterior simulation | Resampling confidence interval |
| Fonte seminale≠ | 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 ↗ | Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185. DOI ↗ |
| Alias | parametrik bootstrap, model-based bootstrap, parametric resampling | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval |
| Correlati | 5 | 5 | 5 |
| Sintesi≠ | 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. | 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. |
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