Regression model
BCa Bootstrap（偏差校正和加速法）
BCa bootstrap是一种重采样方法，由Bradley Efron于1987年提出，通过应用偏差校正和加速调整，可以比普通百分位bootstrap法生成更精确的置信区间。它适用于偏态分布和小样本。
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Method map
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来源
- Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185. DOI: 10.1080/01621459.1987.10478410 ↗
- DiCiccio, T. J. & Efron, B. (1996). Bootstrap Confidence Intervals. Statistical Science, 11(3), 189-228. DOI: 10.1214/ss/1032280214 ↗
如何引用本页
ScholarGate. (2026, June 1). Bias-Corrected and Accelerated Bootstrap. ScholarGate. https://scholargate.app/zh/statistics/bca-bootstrap
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 贝叶斯自助法（Bayesian Bootstrap，由 Rubin 提出）统计学↔ compare
- Bootstrap Inference统计学↔ compare
- 双重（迭代）自助法统计学↔ compare
- 置换 (随机化) 检验统计学↔ compare
- Wild Bootstrap for Regression Inference统计学↔ compare