Regression model
Double (Iterated) Bootstrap
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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Sources
- Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452. DOI: 10.1214/aos/1176350168 ↗
- Beran, R. (1987). Prepivoting to Reduce Level Error of Confidence Sets. Biometrika, 74(3), 457-468. DOI: 10.1093/biomet/74.3.457 ↗