Regression model

BCa Bootstrap (Bias-Corrected and Accelerated)

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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Sources

  1. Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185. DOI: 10.1080/01621459.1987.10478410
  2. DiCiccio, T. J. & Efron, B. (1996). Bootstrap Confidence Intervals. Statistical Science, 11(3), 189-228. DOI: 10.1214/ss/1032280214

Related methods

Referenced by

ScholarGateBCa Bootstrap (Bias-Corrected and Accelerated Bootstrap). Retrieved 2026-06-04 from https://scholargate.app/tr/statistics/bca-bootstrap