Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| BCa Bootstrap (Sesgado y Acelerado Corregido)× | Bootstrap bayesiano (Rubin)× | |
|---|---|---|
| Campo | Estadística | Estadística |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1987 | 1981 |
| Autor original≠ | Bradley Efron | Rubin (1981); large-sample theory by Lo (1987) |
| Tipo≠ | Resampling confidence interval | Resampling / posterior simulation |
| Fuente seminal≠ | Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185. DOI ↗ | Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI ↗ |
| Alias | BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap |
| Relacionados | 5 | 5 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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