Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Bootstrap Bayesiano (Rubin)× | Bootstrap Duplo (Iterado)× | |
|---|---|---|
| Área | Estatística | Estatística |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1981 | 1986 |
| Autor original≠ | Rubin (1981); large-sample theory by Lo (1987) | Hall (1986); Beran (1987) |
| Tipo≠ | Resampling / posterior simulation | Resampling calibration (nested bootstrap) |
| Fonte seminal≠ | Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI ↗ | Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452. DOI ↗ |
| Outros nomes≠ | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap) |
| Relacionados | 5 | 5 |
| Resumo≠ | 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 double bootstrap is a resampling method that calibrates a bootstrap confidence interval with a second, nested layer of bootstrap to bring its actual coverage closer to the nominal level. Introduced by Hall (1986) and Beran (1987), it is especially valuable for small samples and skewed distributions where a single-layer bootstrap under-covers. |
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