Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Bootstrap salvaje para inferencia en regresión× | Inferencia Bootstrap× | |
|---|---|---|
| Campo | Estadística | Estadística |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1986 | 1979 |
| Autor original≠ | Wu (1986); refined by Davidson & Flachaire (2008) | Bradley Efron |
| Tipo≠ | Resampling-based regression inference | Resampling-based inference |
| Fuente seminal≠ | Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295. DOI ↗ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ |
| Alias | wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı |
| Relacionados | 5 | 5 |
| Resumen≠ | The wild bootstrap is a resampling method for regression models with heteroscedastic errors, introduced by Wu (1986) and refined by Davidson and Flachaire (2008). It builds a bootstrap distribution by rescaling each fitted residual with a random sign, so that standard errors and confidence intervals stay valid when the error variance is not constant or the data are clustered. | 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. |
| ScholarGateConjunto de datos ↗ |
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